From 99357682cc7b833c37e033ff9cdfd9dc9238bf94 Mon Sep 17 00:00:00 2001
From: dgunter <dgunter@users.noreply.github.com>
Date: Sat, 5 Sep 2026 17:13:29 -0500
Subject: [PATCH 1/2] Port the notebooks to Python 3, add a ParseZeekLogs
 version, preserve the posts

The 2017-2018 notebooks now run on Python 3.10+ with current pandas,
matplotlib and pyshark (which needs its own event loop on Python 3.12+ and
inside Jupyter). A new notebook redoes the HTTP hunts with ParseZeekLogs and
pandas and also runs on Zeek 8.2.2 TSV and JSON logs. The four Threat
Hunting with Python posts are recovered from the Wayback Machine and the
live Dragos copies into docs/ with their images. uv project, ruff lint of
code cells, and a GitHub Actions workflow that executes every notebook.

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 .github/workflows/notebooks.yml               |   43 +
 .gitignore                                    |   10 +-
 .../Bro HTTP Nmap Indicator Detection.ipynb   |  821 ++--
 ...ing Nmap Behavior with Bro HTTP Logs.ipynb | 3256 +++++-----------
 ...ing Nmap Behavior with ParseZeekLogs.ipynb |  686 ++++
 .../suspicious_http_records_parsezeeklogs.csv |  300 ++
 README.md                                     |   83 +-
 TDS Analysis/tds.ipynb                        |  198 +-
 ...-09-threat-hunting-with-python-prologue.md |   57 +
 ...unting-with-python-part-2-nmap-bro-http.md |   67 +
 ...t-hunting-with-python-part-3-taming-smb.md |   59 +
 ...ting-with-python-part-4-mssql-historian.md |   69 +
 docs/images/part2-1.png                       |  Bin 0 -> 275369 bytes
 docs/images/part2-2.png                       |  Bin 0 -> 290426 bytes
 docs/images/part2-3.png                       |  Bin 0 -> 309550 bytes
 docs/images/part2-4.png                       |  Bin 0 -> 179048 bytes
 docs/images/part2-5.png                       |  Bin 0 -> 339902 bytes
 docs/images/part3-1.png                       |  Bin 0 -> 742177 bytes
 docs/images/part3-2.png                       |  Bin 0 -> 251436 bytes
 docs/images/part3-3.png                       |  Bin 0 -> 727499 bytes
 docs/images/part3-4.png                       |  Bin 0 -> 532719 bytes
 docs/images/part3-5.png                       |  Bin 0 -> 526508 bytes
 docs/images/part3-6.png                       |  Bin 0 -> 153102 bytes
 docs/images/part4-1.png                       |  Bin 0 -> 80434 bytes
 docs/images/part4-2.png                       |  Bin 0 -> 790730 bytes
 docs/images/part4-3.png                       |  Bin 0 -> 916516 bytes
 docs/images/part4-4.png                       |  Bin 0 -> 93302 bytes
 docs/images/part4-5.png                       |  Bin 0 -> 148711 bytes
 docs/images/part4-6.png                       |  Bin 0 -> 68839 bytes
 docs/images/part4-7.png                       |  Bin 0 -> 199089 bytes
 docs/images/prologue-1.png                    |  Bin 0 -> 113400 bytes
 docs/images/prologue-2.png                    |  Bin 0 -> 166020 bytes
 docs/images/prologue-3.png                    |  Bin 0 -> 89241 bytes
 docs/images/prologue-4.png                    |  Bin 0 -> 227148 bytes
 pyproject.toml                                |   39 +
 uv.lock                                       | 3403 +++++++++++++++++
 36 files changed, 6248 insertions(+), 2843 deletions(-)
 create mode 100644 .github/workflows/notebooks.yml
 create mode 100644 Bro HTTP Log Analysis/Detecting Nmap Behavior with ParseZeekLogs.ipynb
 create mode 100644 Bro HTTP Log Analysis/suspicious_http_records_parsezeeklogs.csv
 create mode 100644 docs/2017-09-threat-hunting-with-python-prologue.md
 create mode 100644 docs/2017-11-threat-hunting-with-python-part-2-nmap-bro-http.md
 create mode 100644 docs/2018-02-threat-hunting-with-python-part-3-taming-smb.md
 create mode 100644 docs/2018-03-threat-hunting-with-python-part-4-mssql-historian.md
 create mode 100644 docs/images/part2-1.png
 create mode 100644 docs/images/part2-2.png
 create mode 100644 docs/images/part2-3.png
 create mode 100644 docs/images/part2-4.png
 create mode 100644 docs/images/part2-5.png
 create mode 100644 docs/images/part3-1.png
 create mode 100644 docs/images/part3-2.png
 create mode 100644 docs/images/part3-3.png
 create mode 100644 docs/images/part3-4.png
 create mode 100644 docs/images/part3-5.png
 create mode 100644 docs/images/part3-6.png
 create mode 100644 docs/images/part4-1.png
 create mode 100644 docs/images/part4-2.png
 create mode 100644 docs/images/part4-3.png
 create mode 100644 docs/images/part4-4.png
 create mode 100644 docs/images/part4-5.png
 create mode 100644 docs/images/part4-6.png
 create mode 100644 docs/images/part4-7.png
 create mode 100644 docs/images/prologue-1.png
 create mode 100644 docs/images/prologue-2.png
 create mode 100644 docs/images/prologue-3.png
 create mode 100644 docs/images/prologue-4.png
 create mode 100644 pyproject.toml
 create mode 100644 uv.lock

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
new file mode 100644
index 0000000..a6e5819
--- /dev/null
+++ b/.github/workflows/notebooks.yml
@@ -0,0 +1,43 @@
+name: Notebooks
+
+on:
+  push:
+    branches: [master]
+  pull_request:
+
+jobs:
+  run:
+    name: Lint and execute
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - uses: astral-sh/setup-uv@v6
+        with:
+          enable-cache: true
+      - name: Install tshark for the TDS notebook
+        run: |
+          sudo apt-get update -q
+          sudo DEBIAN_FRONTEND=noninteractive apt-get install -y -q tshark
+      - name: Install the Python environment
+        run: uv sync --frozen
+      - name: Lint code cells
+        run: uv run ruff check .
+      - name: Execute every notebook
+        run: |
+          uv run jupyter nbconvert --to notebook --execute --output-dir /tmp/executed \
+            --ExecutePreprocessor.timeout=600 \
+            "Bro HTTP Log Analysis/"*.ipynb "TDS Analysis/"*.ipynb
+      - name: Fail on cells that wrote to stderr
+        run: |
+          uv run python - <<'PY'
+          import json, pathlib, sys
+          bad = []
+          for nb in pathlib.Path("/tmp/executed").glob("*.ipynb"):
+              cells = json.loads(nb.read_text())["cells"]
+              for i, c in enumerate(cells):
+                  for o in c.get("outputs", []):
+                      if o.get("name") == "stderr":
+                          bad.append(f"{nb.name} cell {i}: {''.join(o['text'])[:200]}")
+          print("\n".join(bad) or "no stderr output")
+          sys.exit(1 if bad else 0)
+          PY
diff --git a/.gitignore b/.gitignore
index 620fea1..baa960d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -40,7 +40,8 @@ htmlcov/
 .tox/
 .coverage
 .coverage.*
-.cache
+.cache/
+.ruff_cache/
 nosetests.xml
 coverage.xml
 *,cover
@@ -88,3 +89,10 @@ ENV/
 
 # Rope project settings
 .ropeproject
+
+# IDEs
+.idea/
+.vscode/
+
+# Notebook execution artefacts
+/tmp/
diff --git a/Bro HTTP Log Analysis/Bro HTTP Nmap Indicator Detection.ipynb b/Bro HTTP Log Analysis/Bro HTTP Nmap Indicator Detection.ipynb
index f67fdca..dd17631 100644
--- a/Bro HTTP Log Analysis/Bro HTTP Nmap Indicator Detection.ipynb	
+++ b/Bro HTTP Log Analysis/Bro HTTP Nmap Indicator Detection.ipynb	
@@ -4,224 +4,240 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:53.540257Z",
+     "iopub.status.busy": "2026-09-05T22:08:53.540065Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.545641Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.544946Z"
+    }
    },
    "outputs": [],
    "source": [
-    "import json\n",
-    "from datetime import datetime, timedelta\n",
-    "import matplotlib.pylab as plot\n",
-    "import numpy as np"
+    "import json"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:53.547771Z",
+     "iopub.status.busy": "2026-09-05T22:08:53.547615Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.553941Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.553464Z"
+    }
    },
    "outputs": [],
    "source": [
     "# Read data from http bro logs\n",
-    "with open(\"http.log\",'r') as infile:\n",
+    "with open(\"http.log\") as infile:\n",
     "    file_data = infile.read()\n",
-    "    \n",
+    "\n",
     "# Split file by newlines\n",
     "file_data = file_data.split('\\n')\n",
     "\n",
     "# Remove comment lines\n",
     "http_data = []\n",
     "for line in file_data:\n",
-    "    if line[0] is not None and line[0] != \"#\":\n",
+    "    if line and not line.startswith(\"#\"):\n",
     "        http_data.append(line)"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:53.555537Z",
+     "iopub.status.busy": "2026-09-05T22:08:53.555447Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.567154Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.566687Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
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@@ -243,20 +259,27 @@
   {
    "cell_type": "code",
    "execution_count": 4,
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+   "metadata": {
+    "execution": {
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+     "iopub.status.busy": "2026-09-05T22:08:53.587575Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.595601Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.595176Z"
+    }
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      "output_type": "stream",
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-      "  \"Wget/1.16.1 (linux-gnu)\": 1\n",
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+      "  \"Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0\": 99,\n",
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       "}\n"
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@@ -278,207 +301,214 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:53.596657Z",
+     "iopub.status.busy": "2026-09-05T22:08:53.596600Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.610634Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.610324Z"
+    }
+   },
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+      "      \"1445422292.319808\": 1,\n",
+      "      \"1445422298.777468\": 1,\n",
+      "      \"1445422298.992366\": 1,\n",
+      "      \"1445422299.890418\": 1,\n",
+      "      \"1445422306.307627\": 1\n",
+      "    },\n",
       "    \"192.168.88.61\": {\n",
-      "      \"1445422300.131605\": 1, \n",
-      "      \"1445422289.591833\": 1, \n",
-      "      \"1445422300.988103\": 1, \n",
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-      "      \"1445422289.866199\": 1, \n",
-      "      \"1445422290.915767\": 1, \n",
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-      "      \"1445422297.244478\": 1, \n",
-      "      \"1445422300.766659\": 1, \n",
-      "      \"1445422301.201058\": 1, \n",
-      "      \"1445422299.466633\": 1, \n",
-      "      \"1445422293.119720\": 1, \n",
-      "      \"1445422300.548608\": 1, \n",
-      "      \"1445422299.890145\": 1, \n",
-      "      \"1445422300.339324\": 1\n",
-      "    }, \n",
+      "      \"1445422289.591833\": 1,\n",
+      "      \"1445422289.866199\": 1,\n",
+      "      \"1445422290.915767\": 1,\n",
+      "      \"1445422292.798306\": 1,\n",
+      "      \"1445422293.119720\": 1,\n",
+      "      \"1445422297.244478\": 1,\n",
+      "      \"1445422299.466633\": 1,\n",
+      "      \"1445422299.679622\": 1,\n",
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+      "      \"1445422300.988103\": 1,\n",
+      "      \"1445422301.201058\": 1\n",
+      "    },\n",
+      "    \"192.168.88.100\": {\n",
+      "      \"1445422290.513640\": 1,\n",
+      "      \"1445422289.380025\": 1,\n",
+      "      \"1445422290.915620\": 1,\n",
+      "      \"1445422297.138751\": 1,\n",
+      "      \"1445422308.102295\": 1\n",
+      "    },\n",
+      "    \"192.168.88.20\": {\n",
+      "      \"1445422290.968135\": 1,\n",
+      "      \"1445422291.885333\": 1,\n",
+      "      \"1445422298.777344\": 1,\n",
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+      "      \"1445422300.078390\": 1,\n",
+      "      \"1445422300.287326\": 1,\n",
+      "      \"1445422300.497165\": 1,\n",
+      "      \"1445422302.855427\": 1,\n",
+      "      \"1445422313.532961\": 1,\n",
+      "      \"1445422315.698055\": 1\n",
+      "    },\n",
+      "    \"192.168.88.115\": {\n",
+      "      \"1445422291.454377\": 1,\n",
+      "      \"1445422291.938518\": 1,\n",
+      "      \"1445422292.693354\": 1,\n",
+      "      \"1445422300.184951\": 1,\n",
+      "      \"1445422300.766784\": 1,\n",
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+      "      \"1445422321.077807\": 1,\n",
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+      "      \"1445422322.355297\": 1\n",
+      "    },\n",
       "    \"192.168.88.95\": {\n",
-      "      \"1445422289.380290\": 1, \n",
-      "      \"1445422344.783066\": 1, \n",
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-      "      \"1445422348.751232\": 1, \n",
-      "      \"1445422290.347162\": 1, \n",
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+      "      \"1445422289.380290\": 1,\n",
       "      \"1445422289.380169\": 1\n",
       "    }\n",
+      "  },\n",
+      "  \"192.168.2.42\": {\n",
+      "    \"192.168.88.115\": {\n",
+      "      \"1445425456.491738\": 1,\n",
+      "      \"1445425456.492152\": 1,\n",
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       "  }\n",
       "}\n"
      ]
@@ -510,7 +540,14 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:53.611582Z",
+     "iopub.status.busy": "2026-09-05T22:08:53.611522Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.613389Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.613049Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -547,7 +584,13 @@
    "cell_type": "code",
    "execution_count": 7,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:53.614175Z",
+     "iopub.status.busy": "2026-09-05T22:08:53.614124Z",
+     "iopub.status.idle": "2026-09-05T22:08:53.629704Z",
+     "shell.execute_reply": "2026-09-05T22:08:53.629284Z"
+    }
    },
    "outputs": [],
    "source": [
@@ -563,27 +606,27 @@
     "            uri = line.split('\\t')[9]\n",
     "            if client in suspicious_hosts.keys():\n",
     "                if server in suspicious_hosts[client]:\n",
-    "                    outfile.write(\"\\\"\" + line.replace(\"\\t\",\"\\\",\\\"\") + \"\\\"\\n\")\n"
+    "                    outfile.write(\"\\\"\" + line.replace(\"\\t\",\"\\\",\\\"\") + \"\\\"\\n\")"
    ]
   }
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.14.7"
   }
  },
  "nbformat": 4,
diff --git a/Bro HTTP Log Analysis/Detecting Nmap Behavior with Bro HTTP Logs.ipynb b/Bro HTTP Log Analysis/Detecting Nmap Behavior with Bro HTTP Logs.ipynb
index d0a3e66..85e9457 100644
--- a/Bro HTTP Log Analysis/Detecting Nmap Behavior with Bro HTTP Logs.ipynb	
+++ b/Bro HTTP Log Analysis/Detecting Nmap Behavior with Bro HTTP Logs.ipynb	
@@ -5,19 +5,22 @@
    "execution_count": 1,
    "metadata": {
     "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.170280Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.170085Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.507743Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.507256Z"
+    },
     "scrolled": true
    },
    "outputs": [],
    "source": [
-    "import json\n",
-    "from datetime import datetime, timedelta\n",
-    "import matplotlib.pylab as plot\n",
-    "import matplotlib.pyplot as plt\n",
-    "from matplotlib import dates\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
+    "from datetime import datetime\n",
     "\n",
     "import matplotlib\n",
+    "import matplotlib.pyplot as plot\n",
+    "import pandas as pd\n",
+    "\n",
     "matplotlib.style.use('ggplot')\n",
     "%matplotlib inline"
    ]
@@ -27,21 +30,27 @@
    "execution_count": 2,
    "metadata": {
     "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.508889Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.508791Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.512111Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.511684Z"
+    },
     "scrolled": true
    },
    "outputs": [],
    "source": [
     "# Read data from http bro logs\n",
-    "with open(\"http.log\",'r') as infile:\n",
+    "with open(\"http.log\") as infile:\n",
     "    file_data = infile.read()\n",
-    "    \n",
+    "\n",
     "# Split file by newlines\n",
     "file_data = file_data.split('\\n')\n",
     "\n",
     "# Remove comment lines\n",
     "http_data = []\n",
     "for line in file_data:\n",
-    "    if line[0] is not None and line[0] != \"#\":\n",
+    "    if line and not line.startswith(\"#\"):\n",
     "        http_data.append(line)"
    ]
   },
@@ -49,6 +58,12 @@
    "cell_type": "code",
    "execution_count": 3,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.513040Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.512984Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.524007Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.523680Z"
+    },
     "scrolled": true
    },
    "outputs": [],
@@ -61,10 +76,10 @@
     "    timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n",
     "    # Strip second and microsecond from timestamp\n",
     "    timestamp = str(timestamp.replace(second=0,microsecond=0))\n",
-    "    \n",
+    "\n",
     "    # Extract the user agent\n",
     "    user_agent = line.split('\\t')[11]\n",
-    "    \n",
+    "\n",
     "    # Update status code analysis variable\n",
     "    if user_agent not in user_agent_analysis.keys():\n",
     "        user_agent_analysis[user_agent] = {timestamp: 1}\n",
@@ -73,7 +88,7 @@
     "            user_agent_analysis[user_agent][timestamp] = 1\n",
     "        else:\n",
     "            user_agent_analysis[user_agent][timestamp] += 1\n",
-    "            \n",
+    "\n",
     "    # Update overall user agent count\n",
     "    if user_agent not in user_agent_overall.keys():\n",
     "        user_agent_overall[user_agent] = 1\n",
@@ -85,6 +100,12 @@
    "cell_type": "code",
    "execution_count": 4,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.525040Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.524981Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.533230Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.532934Z"
+    },
     "scrolled": false
    },
    "outputs": [
@@ -92,30 +113,30 @@
      "data": {
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        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
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        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
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        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>-</th>\n",
+       "      <th>Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)</th>\n",
        "      <th>Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36</th>\n",
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-       "      <th>Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)</th>\n",
        "      <th>Wget/1.16.1 (linux-gnu)</th>\n",
+       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0</th>\n",
+       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
@@ -142,28 +163,18 @@
        "    <tr>\n",
        "      <th>2015-10-21 05:11:00</th>\n",
        "      <td>21.0</td>\n",
+       "      <td>132.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
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        "    <tr>\n",
        "      <th>2015-10-21 05:12:00</th>\n",
        "      <td>7.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>21.0</td>\n",
        "      <td>0.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2015-10-21 06:02:00</th>\n",
-       "      <td>0.0</td>\n",
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@@ -180,79 +191,267 @@
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        "    <tr>\n",
-       "      <th>2015-10-21 06:04:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:05:00</th>\n",
-       "      <td>19.0</td>\n",
-       "      <td>377.0</td>\n",
+       "      <th>2015-10-21 11:50:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:06:00</th>\n",
+       "      <th>2015-10-21 11:51:00</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1196.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:07:00</th>\n",
+       "      <th>2015-10-21 11:52:00</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>993.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
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        "    <tr>\n",
-       "      <th>2015-10-21 06:08:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1171.0</td>\n",
+       "      <th>2015-10-21 11:53:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:09:00</th>\n",
+       "      <th>2015-10-21 11:54:00</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>1017.0</td>\n",
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        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>70 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                        -  \\\n",
+       "2015-10-21 05:09:00  29.0   \n",
+       "2015-10-21 05:10:00   2.0   \n",
+       "2015-10-21 05:11:00  21.0   \n",
+       "2015-10-21 05:12:00   7.0   \n",
+       "2015-10-21 06:03:00   1.0   \n",
+       "...                   ...   \n",
+       "2015-10-21 11:50:00   0.0   \n",
+       "2015-10-21 11:51:00   0.0   \n",
+       "2015-10-21 11:52:00   0.0   \n",
+       "2015-10-21 11:53:00   0.0   \n",
+       "2015-10-21 11:54:00   0.0   \n",
+       "\n",
+       "                     Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)  \\\n",
+       "2015-10-21 05:09:00                                                0.0                                \n",
+       "2015-10-21 05:10:00                                                0.0                                \n",
+       "2015-10-21 05:11:00                                              132.0                                \n",
+       "2015-10-21 05:12:00                                               21.0                                \n",
+       "2015-10-21 06:03:00                                                0.0                                \n",
+       "...                                                                ...                                \n",
+       "2015-10-21 11:50:00                                                0.0                                \n",
+       "2015-10-21 11:51:00                                                0.0                                \n",
+       "2015-10-21 11:52:00                                                0.0                                \n",
+       "2015-10-21 11:53:00                                                0.0                                \n",
+       "2015-10-21 11:54:00                                                0.0                                \n",
+       "\n",
+       "                     Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36  \\\n",
+       "2015-10-21 05:09:00                                                0.0                                                          \n",
+       "2015-10-21 05:10:00                                                0.0                                                          \n",
+       "2015-10-21 05:11:00                                                0.0                                                          \n",
+       "2015-10-21 05:12:00                                                0.0                                                          \n",
+       "2015-10-21 06:03:00                                                0.0                                                          \n",
+       "...                                                                ...                                                          \n",
+       "2015-10-21 11:50:00                                                0.0                                                          \n",
+       "2015-10-21 11:51:00                                                0.0                                                          \n",
+       "2015-10-21 11:52:00                                                0.0                                                          \n",
+       "2015-10-21 11:53:00                                                0.0                                                          \n",
+       "2015-10-21 11:54:00                                                0.0                                                          \n",
+       "\n",
+       "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0  \\\n",
+       "2015-10-21 05:09:00                                                0.0                              \n",
+       "2015-10-21 05:10:00                                                0.0                              \n",
+       "2015-10-21 05:11:00                                                0.0                              \n",
+       "2015-10-21 05:12:00                                                0.0                              \n",
+       "2015-10-21 06:03:00                                                0.0                              \n",
+       "...                                                                ...                              \n",
+       "2015-10-21 11:50:00                                                0.0                              \n",
+       "2015-10-21 11:51:00                                                0.0                              \n",
+       "2015-10-21 11:52:00                                                0.0                              \n",
+       "2015-10-21 11:53:00                                                0.0                              \n",
+       "2015-10-21 11:54:00                                                0.0                              \n",
+       "\n",
+       "                     Wget/1.16.1 (linux-gnu)  \\\n",
+       "2015-10-21 05:09:00                      0.0   \n",
+       "2015-10-21 05:10:00                      0.0   \n",
+       "2015-10-21 05:11:00                      0.0   \n",
+       "2015-10-21 05:12:00                      0.0   \n",
+       "2015-10-21 06:03:00                      0.0   \n",
+       "...                                      ...   \n",
+       "2015-10-21 11:50:00                      0.0   \n",
+       "2015-10-21 11:51:00                      0.0   \n",
+       "2015-10-21 11:52:00                      0.0   \n",
+       "2015-10-21 11:53:00                      0.0   \n",
+       "2015-10-21 11:54:00                      0.0   \n",
+       "\n",
+       "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0  \\\n",
+       "2015-10-21 05:09:00                                                0.0                              \n",
+       "2015-10-21 05:10:00                                                0.0                              \n",
+       "2015-10-21 05:11:00                                                0.0                              \n",
+       "2015-10-21 05:12:00                                                0.0                              \n",
+       "2015-10-21 06:03:00                                                0.0                              \n",
+       "...                                                                ...                              \n",
+       "2015-10-21 11:50:00                                                6.0                              \n",
+       "2015-10-21 11:51:00                                                5.0                              \n",
+       "2015-10-21 11:52:00                                                6.0                              \n",
+       "2015-10-21 11:53:00                                                5.0                              \n",
+       "2015-10-21 11:54:00                                                4.0                              \n",
+       "\n",
+       "                     Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0  \n",
+       "2015-10-21 05:09:00                                                0.0                           \n",
+       "2015-10-21 05:10:00                                                0.0                           \n",
+       "2015-10-21 05:11:00                                                0.0                           \n",
+       "2015-10-21 05:12:00                                                0.0                           \n",
+       "2015-10-21 06:03:00                                                0.0                           \n",
+       "...                                                                ...                           \n",
+       "2015-10-21 11:50:00                                                0.0                           \n",
+       "2015-10-21 11:51:00                                                0.0                           \n",
+       "2015-10-21 11:52:00                                                0.0                           \n",
+       "2015-10-21 11:53:00                                               12.0                           \n",
+       "2015-10-21 11:54:00                                                0.0                           \n",
+       "\n",
+       "[70 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.DataFrame.from_dict(user_agent_analysis,orient='columns').fillna(0)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.534219Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.534166Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.672771Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.672431Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#df.plot(figsize=(12,9))\n",
+    "\n",
+    "ax = df.plot(rot=90,figsize=(12,9))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.673892Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.673816Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.682417Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.682100Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['-', 'Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0', 'Wget/1.16.1 (linux-gnu)', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0', 'Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>-</th>\n",
+       "      <th>Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)</th>\n",
+       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0</th>\n",
+       "      <th>Wget/1.16.1 (linux-gnu)</th>\n",
+       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0</th>\n",
+       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:10:00</th>\n",
+       "      <th>2015-10-21 05:09:00</th>\n",
+       "      <td>29.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>19.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:11:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
+       "      <th>2015-10-21 05:10:00</th>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -260,29 +459,26 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:12:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 05:11:00</th>\n",
+       "      <td>21.0</td>\n",
+       "      <td>132.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:13:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 05:12:00</th>\n",
+       "      <td>7.0</td>\n",
+       "      <td>21.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:14:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>63.0</td>\n",
+       "      <th>2015-10-21 06:03:00</th>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -290,29 +486,26 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:16:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>66.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 06:04:00</th>\n",
+       "      <td>1.0</td>\n",
+       "      <td>15.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:17:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 06:05:00</th>\n",
        "      <td>19.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:18:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.0</td>\n",
+       "      <th>2015-10-21 07:01:00</th>\n",
+       "      <td>12.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -320,9 +513,8 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:19:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>2015-10-21 07:02:00</th>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -330,9 +522,8 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:20:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>55.0</td>\n",
+       "      <th>2015-10-21 10:35:00</th>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -340,9 +531,8 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:21:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
+       "      <th>2015-10-21 11:19:00</th>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -350,9 +540,8 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:24:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
+       "      <th>2015-10-21 11:36:00</th>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -360,9 +549,8 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:25:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.0</td>\n",
+       "      <th>2015-10-21 11:37:00</th>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -370,11 +558,10 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 06:26:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
+       "      <th>2015-10-21 06:10:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -383,190 +570,198 @@
        "      <th>2015-10-21 06:27:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>14.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-10-21 06:28:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>28.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-10-21 06:29:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 07:01:00</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 07:42:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
+       "      <th>2015-10-21 07:43:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:34:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 07:59:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
+       "      <td>16.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:35:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 08:00:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:36:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 08:01:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>44.0</td>\n",
+       "      <td>16.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:37:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 10:29:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>43.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:38:00</th>\n",
+       "      <th>2015-10-21 10:31:00</th>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-21 10:32:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:44:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 10:33:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>38.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 10:45:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 10:34:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>33.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:10:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 10:36:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>25.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>44.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:11:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 10:37:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>11.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>43.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:19:00</th>\n",
+       "      <th>2015-10-21 10:38:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-21 10:44:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>38.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:22:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 10:45:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>33.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:24:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 11:10:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>25.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:36:00</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 11:11:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:37:00</th>\n",
-       "      <td>1.0</td>\n",
+       "      <th>2015-10-21 11:22:00</th>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2015-10-21 11:24:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>3.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -574,9 +769,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>18.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -584,9 +778,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>12.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -594,9 +787,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -604,9 +796,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -614,9 +805,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -624,9 +814,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -634,9 +823,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -644,9 +832,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -654,9 +841,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -664,9 +850,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -674,9 +859,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -684,9 +868,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -694,9 +877,8 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
@@ -704,34 +886,30 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-10-21 11:53:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2015-10-21 11:54:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>70 rows × 7 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
@@ -740,35 +918,29 @@
        "2015-10-21 05:10:00   2.0   \n",
        "2015-10-21 05:11:00  21.0   \n",
        "2015-10-21 05:12:00   7.0   \n",
-       "2015-10-21 06:02:00   0.0   \n",
        "2015-10-21 06:03:00   1.0   \n",
        "2015-10-21 06:04:00   1.0   \n",
        "2015-10-21 06:05:00  19.0   \n",
-       "2015-10-21 06:06:00   0.0   \n",
-       "2015-10-21 06:07:00   0.0   \n",
-       "2015-10-21 06:08:00   0.0   \n",
-       "2015-10-21 06:09:00   0.0   \n",
+       "2015-10-21 07:01:00  12.0   \n",
+       "2015-10-21 07:02:00   1.0   \n",
+       "2015-10-21 10:35:00   1.0   \n",
+       "2015-10-21 11:19:00   2.0   \n",
+       "2015-10-21 11:36:00   6.0   \n",
+       "2015-10-21 11:37:00   1.0   \n",
        "2015-10-21 06:10:00   0.0   \n",
-       "2015-10-21 06:11:00   0.0   \n",
-       "2015-10-21 06:12:00   0.0   \n",
-       "2015-10-21 06:13:00   0.0   \n",
-       "2015-10-21 06:14:00   0.0   \n",
-       "2015-10-21 06:16:00   0.0   \n",
-       "2015-10-21 06:17:00   0.0   \n",
-       "2015-10-21 06:18:00   0.0   \n",
-       "2015-10-21 06:19:00   0.0   \n",
-       "2015-10-21 06:20:00   0.0   \n",
-       "2015-10-21 06:21:00   0.0   \n",
-       "2015-10-21 06:24:00   0.0   \n",
-       "2015-10-21 06:25:00   0.0   \n",
-       "2015-10-21 06:26:00   0.0   \n",
        "2015-10-21 06:27:00   0.0   \n",
        "2015-10-21 06:28:00   0.0   \n",
        "2015-10-21 06:29:00   0.0   \n",
-       "2015-10-21 07:01:00  12.0   \n",
-       "...                   ...   \n",
+       "2015-10-21 07:42:00   0.0   \n",
+       "2015-10-21 07:43:00   0.0   \n",
+       "2015-10-21 07:59:00   0.0   \n",
+       "2015-10-21 08:00:00   0.0   \n",
+       "2015-10-21 08:01:00   0.0   \n",
+       "2015-10-21 10:29:00   0.0   \n",
+       "2015-10-21 10:31:00   0.0   \n",
+       "2015-10-21 10:32:00   0.0   \n",
+       "2015-10-21 10:33:00   0.0   \n",
        "2015-10-21 10:34:00   0.0   \n",
-       "2015-10-21 10:35:00   1.0   \n",
        "2015-10-21 10:36:00   0.0   \n",
        "2015-10-21 10:37:00   0.0   \n",
        "2015-10-21 10:38:00   0.0   \n",
@@ -776,11 +948,8 @@
        "2015-10-21 10:45:00   0.0   \n",
        "2015-10-21 11:10:00   0.0   \n",
        "2015-10-21 11:11:00   0.0   \n",
-       "2015-10-21 11:19:00   2.0   \n",
        "2015-10-21 11:22:00   0.0   \n",
        "2015-10-21 11:24:00   0.0   \n",
-       "2015-10-21 11:36:00   6.0   \n",
-       "2015-10-21 11:37:00   1.0   \n",
        "2015-10-21 11:39:00   0.0   \n",
        "2015-10-21 11:40:00   0.0   \n",
        "2015-10-21 11:41:00   0.0   \n",
@@ -798,229 +967,88 @@
        "2015-10-21 11:53:00   0.0   \n",
        "2015-10-21 11:54:00   0.0   \n",
        "\n",
-       "                     Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36  \\\n",
-       "2015-10-21 05:09:00                                                0.0                                                          \n",
-       "2015-10-21 05:10:00                                                0.0                                                          \n",
-       "2015-10-21 05:11:00                                                0.0                                                          \n",
-       "2015-10-21 05:12:00                                                0.0                                                          \n",
-       "2015-10-21 06:02:00                                                4.0                                                          \n",
-       "2015-10-21 06:03:00                                                0.0                                                          \n",
-       "2015-10-21 06:04:00                                                2.0                                                          \n",
-       "2015-10-21 06:05:00                                              377.0                                                          \n",
-       "2015-10-21 06:06:00                                             1196.0                                                          \n",
-       "2015-10-21 06:07:00                                              993.0                                                          \n",
-       "2015-10-21 06:08:00                                             1171.0                                                          \n",
-       "2015-10-21 06:09:00                                             1017.0                                                          \n",
-       "2015-10-21 06:10:00                                               19.0                                                          \n",
-       "2015-10-21 06:11:00                                               18.0                                                          \n",
-       "2015-10-21 06:12:00                                                2.0                                                          \n",
-       "2015-10-21 06:13:00                                               13.0                                                          \n",
-       "2015-10-21 06:14:00                                               63.0                                                          \n",
-       "2015-10-21 06:16:00                                               66.0                                                          \n",
-       "2015-10-21 06:17:00                                               19.0                                                          \n",
-       "2015-10-21 06:18:00                                                9.0                                                          \n",
-       "2015-10-21 06:19:00                                                2.0                                                          \n",
-       "2015-10-21 06:20:00                                               55.0                                                          \n",
-       "2015-10-21 06:21:00                                                3.0                                                          \n",
-       "2015-10-21 06:24:00                                                4.0                                                          \n",
-       "2015-10-21 06:25:00                                               10.0                                                          \n",
-       "2015-10-21 06:26:00                                                2.0                                                          \n",
-       "2015-10-21 06:27:00                                                0.0                                                          \n",
-       "2015-10-21 06:28:00                                                0.0                                                          \n",
-       "2015-10-21 06:29:00                                                0.0                                                          \n",
-       "2015-10-21 07:01:00                                                0.0                                                          \n",
-       "...                                                                ...                                                          \n",
-       "2015-10-21 10:34:00                                                0.0                                                          \n",
-       "2015-10-21 10:35:00                                                0.0                                                          \n",
-       "2015-10-21 10:36:00                                                0.0                                                          \n",
-       "2015-10-21 10:37:00                                                0.0                                                          \n",
-       "2015-10-21 10:38:00                                                0.0                                                          \n",
-       "2015-10-21 10:44:00                                                0.0                                                          \n",
-       "2015-10-21 10:45:00                                                0.0                                                          \n",
-       "2015-10-21 11:10:00                                                0.0                                                          \n",
-       "2015-10-21 11:11:00                                                0.0                                                          \n",
-       "2015-10-21 11:19:00                                                0.0                                                          \n",
-       "2015-10-21 11:22:00                                                0.0                                                          \n",
-       "2015-10-21 11:24:00                                                0.0                                                          \n",
-       "2015-10-21 11:36:00                                                0.0                                                          \n",
-       "2015-10-21 11:37:00                                                0.0                                                          \n",
-       "2015-10-21 11:39:00                                                0.0                                                          \n",
-       "2015-10-21 11:40:00                                                0.0                                                          \n",
-       "2015-10-21 11:41:00                                                0.0                                                          \n",
-       "2015-10-21 11:42:00                                                0.0                                                          \n",
-       "2015-10-21 11:43:00                                                0.0                                                          \n",
-       "2015-10-21 11:44:00                                                0.0                                                          \n",
-       "2015-10-21 11:45:00                                                0.0                                                          \n",
-       "2015-10-21 11:46:00                                                0.0                                                          \n",
-       "2015-10-21 11:47:00                                                0.0                                                          \n",
-       "2015-10-21 11:48:00                                                0.0                                                          \n",
-       "2015-10-21 11:49:00                                                0.0                                                          \n",
-       "2015-10-21 11:50:00                                                0.0                                                          \n",
-       "2015-10-21 11:51:00                                                0.0                                                          \n",
-       "2015-10-21 11:52:00                                                0.0                                                          \n",
-       "2015-10-21 11:53:00                                                0.0                                                          \n",
-       "2015-10-21 11:54:00                                                0.0                                                          \n",
-       "\n",
-       "                     Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0  \\\n",
-       "2015-10-21 05:09:00                                                0.0                            \n",
-       "2015-10-21 05:10:00                                                0.0                            \n",
-       "2015-10-21 05:11:00                                                0.0                            \n",
-       "2015-10-21 05:12:00                                                0.0                            \n",
-       "2015-10-21 06:02:00                                                0.0                            \n",
-       "2015-10-21 06:03:00                                                0.0                            \n",
-       "2015-10-21 06:04:00                                                0.0                            \n",
-       "2015-10-21 06:05:00                                                0.0                            \n",
-       "2015-10-21 06:06:00                                                0.0                            \n",
-       "2015-10-21 06:07:00                                                0.0                            \n",
-       "2015-10-21 06:08:00                                                0.0                            \n",
-       "2015-10-21 06:09:00                                                0.0                            \n",
-       "2015-10-21 06:10:00                                                0.0                            \n",
-       "2015-10-21 06:11:00                                                0.0                            \n",
-       "2015-10-21 06:12:00                                                0.0                            \n",
-       "2015-10-21 06:13:00                                                0.0                            \n",
-       "2015-10-21 06:14:00                                                0.0                            \n",
-       "2015-10-21 06:16:00                                                0.0                            \n",
-       "2015-10-21 06:17:00                                                0.0                            \n",
-       "2015-10-21 06:18:00                                                0.0                            \n",
-       "2015-10-21 06:19:00                                                0.0                            \n",
-       "2015-10-21 06:20:00                                                0.0                            \n",
-       "2015-10-21 06:21:00                                                0.0                            \n",
-       "2015-10-21 06:24:00                                                0.0                            \n",
-       "2015-10-21 06:25:00                                                0.0                            \n",
-       "2015-10-21 06:26:00                                                0.0                            \n",
-       "2015-10-21 06:27:00                                                0.0                            \n",
-       "2015-10-21 06:28:00                                                0.0                            \n",
-       "2015-10-21 06:29:00                                                0.0                            \n",
-       "2015-10-21 07:01:00                                                0.0                            \n",
-       "...                                                                ...                            \n",
-       "2015-10-21 10:34:00                                                0.0                            \n",
-       "2015-10-21 10:35:00                                                0.0                            \n",
-       "2015-10-21 10:36:00                                                0.0                            \n",
-       "2015-10-21 10:37:00                                                0.0                            \n",
-       "2015-10-21 10:38:00                                                0.0                            \n",
-       "2015-10-21 10:44:00                                                0.0                            \n",
-       "2015-10-21 10:45:00                                                0.0                            \n",
-       "2015-10-21 11:10:00                                                0.0                            \n",
-       "2015-10-21 11:11:00                                                0.0                            \n",
-       "2015-10-21 11:19:00                                                0.0                            \n",
-       "2015-10-21 11:22:00                                                0.0                            \n",
-       "2015-10-21 11:24:00                                                0.0                            \n",
-       "2015-10-21 11:36:00                                                0.0                            \n",
-       "2015-10-21 11:37:00                                                0.0                            \n",
-       "2015-10-21 11:39:00                                                0.0                            \n",
-       "2015-10-21 11:40:00                                                0.0                            \n",
-       "2015-10-21 11:41:00                                                0.0                            \n",
-       "2015-10-21 11:42:00                                                0.0                            \n",
-       "2015-10-21 11:43:00                                                0.0                            \n",
-       "2015-10-21 11:44:00                                                0.0                            \n",
-       "2015-10-21 11:45:00                                                0.0                            \n",
-       "2015-10-21 11:46:00                                                0.0                            \n",
-       "2015-10-21 11:47:00                                                0.0                            \n",
-       "2015-10-21 11:48:00                                                0.0                            \n",
-       "2015-10-21 11:49:00                                                0.0                            \n",
-       "2015-10-21 11:50:00                                                0.0                            \n",
-       "2015-10-21 11:51:00                                                0.0                            \n",
-       "2015-10-21 11:52:00                                                0.0                            \n",
-       "2015-10-21 11:53:00                                               12.0                            \n",
-       "2015-10-21 11:54:00                                                0.0                            \n",
-       "\n",
-       "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0  \\\n",
-       "2015-10-21 05:09:00                                                0.0                              \n",
-       "2015-10-21 05:10:00                                                0.0                              \n",
-       "2015-10-21 05:11:00                                                0.0                              \n",
-       "2015-10-21 05:12:00                                                0.0                              \n",
-       "2015-10-21 06:02:00                                                0.0                              \n",
-       "2015-10-21 06:03:00                                                0.0                              \n",
-       "2015-10-21 06:04:00                                                0.0                              \n",
-       "2015-10-21 06:05:00                                                0.0                              \n",
-       "2015-10-21 06:06:00                                                0.0                              \n",
-       "2015-10-21 06:07:00                                                0.0                              \n",
-       "2015-10-21 06:08:00                                                0.0                              \n",
-       "2015-10-21 06:09:00                                                0.0                              \n",
-       "2015-10-21 06:10:00                                                0.0                              \n",
-       "2015-10-21 06:11:00                                                0.0                              \n",
-       "2015-10-21 06:12:00                                                0.0                              \n",
-       "2015-10-21 06:13:00                                                0.0                              \n",
-       "2015-10-21 06:14:00                                                0.0                              \n",
-       "2015-10-21 06:16:00                                                0.0                              \n",
-       "2015-10-21 06:17:00                                                0.0                              \n",
-       "2015-10-21 06:18:00                                                0.0                              \n",
-       "2015-10-21 06:19:00                                                0.0                              \n",
-       "2015-10-21 06:20:00                                                0.0                              \n",
-       "2015-10-21 06:21:00                                                0.0                              \n",
-       "2015-10-21 06:24:00                                                0.0                              \n",
-       "2015-10-21 06:25:00                                                0.0                              \n",
-       "2015-10-21 06:26:00                                                0.0                              \n",
-       "2015-10-21 06:27:00                                                0.0                              \n",
-       "2015-10-21 06:28:00                                                0.0                              \n",
-       "2015-10-21 06:29:00                                                0.0                              \n",
-       "2015-10-21 07:01:00                                                0.0                              \n",
-       "...                                                                ...                              \n",
-       "2015-10-21 10:34:00                                                1.0                              \n",
-       "2015-10-21 10:35:00                                                0.0                              \n",
-       "2015-10-21 10:36:00                                               44.0                              \n",
-       "2015-10-21 10:37:00                                               43.0                              \n",
-       "2015-10-21 10:38:00                                                2.0                              \n",
-       "2015-10-21 10:44:00                                               38.0                              \n",
-       "2015-10-21 10:45:00                                               33.0                              \n",
-       "2015-10-21 11:10:00                                               25.0                              \n",
-       "2015-10-21 11:11:00                                               11.0                              \n",
-       "2015-10-21 11:19:00                                                0.0                              \n",
-       "2015-10-21 11:22:00                                                3.0                              \n",
-       "2015-10-21 11:24:00                                                3.0                              \n",
-       "2015-10-21 11:36:00                                                0.0                              \n",
-       "2015-10-21 11:37:00                                                0.0                              \n",
-       "2015-10-21 11:39:00                                               18.0                              \n",
-       "2015-10-21 11:40:00                                               12.0                              \n",
-       "2015-10-21 11:41:00                                                8.0                              \n",
-       "2015-10-21 11:42:00                                                6.0                              \n",
-       "2015-10-21 11:43:00                                                6.0                              \n",
-       "2015-10-21 11:44:00                                                5.0                              \n",
-       "2015-10-21 11:45:00                                                6.0                              \n",
-       "2015-10-21 11:46:00                                                6.0                              \n",
-       "2015-10-21 11:47:00                                                5.0                              \n",
-       "2015-10-21 11:48:00                                                6.0                              \n",
-       "2015-10-21 11:49:00                                                6.0                              \n",
-       "2015-10-21 11:50:00                                                6.0                              \n",
-       "2015-10-21 11:51:00                                                5.0                              \n",
-       "2015-10-21 11:52:00                                                6.0                              \n",
-       "2015-10-21 11:53:00                                                5.0                              \n",
-       "2015-10-21 11:54:00                                                4.0                              \n",
+       "                     Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)  \\\n",
+       "2015-10-21 05:09:00                                                0.0                                \n",
+       "2015-10-21 05:10:00                                                0.0                                \n",
+       "2015-10-21 05:11:00                                              132.0                                \n",
+       "2015-10-21 05:12:00                                               21.0                                \n",
+       "2015-10-21 06:03:00                                                0.0                                \n",
+       "2015-10-21 06:04:00                                               15.0                                \n",
+       "2015-10-21 06:05:00                                                3.0                                \n",
+       "2015-10-21 07:01:00                                                0.0                                \n",
+       "2015-10-21 07:02:00                                                0.0                                \n",
+       "2015-10-21 10:35:00                                                0.0                                \n",
+       "2015-10-21 11:19:00                                                0.0                                \n",
+       "2015-10-21 11:36:00                                                0.0                                \n",
+       "2015-10-21 11:37:00                                                0.0                                \n",
+       "2015-10-21 06:10:00                                                0.0                                \n",
+       "2015-10-21 06:27:00                                                0.0                                \n",
+       "2015-10-21 06:28:00                                                0.0                                \n",
+       "2015-10-21 06:29:00                                                0.0                                \n",
+       "2015-10-21 07:42:00                                                0.0                                \n",
+       "2015-10-21 07:43:00                                                0.0                                \n",
+       "2015-10-21 07:59:00                                                0.0                                \n",
+       "2015-10-21 08:00:00                                                0.0                                \n",
+       "2015-10-21 08:01:00                                                0.0                                \n",
+       "2015-10-21 10:29:00                                                0.0                                \n",
+       "2015-10-21 10:31:00                                                0.0                                \n",
+       "2015-10-21 10:32:00                                                0.0                                \n",
+       "2015-10-21 10:33:00                                                0.0                                \n",
+       "2015-10-21 10:34:00                                                0.0                                \n",
+       "2015-10-21 10:36:00                                                0.0                                \n",
+       "2015-10-21 10:37:00                                                0.0                                \n",
+       "2015-10-21 10:38:00                                                0.0                                \n",
+       "2015-10-21 10:44:00                                                0.0                                \n",
+       "2015-10-21 10:45:00                                                0.0                                \n",
+       "2015-10-21 11:10:00                                                0.0                                \n",
+       "2015-10-21 11:11:00                                                0.0                                \n",
+       "2015-10-21 11:22:00                                                0.0                                \n",
+       "2015-10-21 11:24:00                                                0.0                                \n",
+       "2015-10-21 11:39:00                                                0.0                                \n",
+       "2015-10-21 11:40:00                                                0.0                                \n",
+       "2015-10-21 11:41:00                                                0.0                                \n",
+       "2015-10-21 11:42:00                                                0.0                                \n",
+       "2015-10-21 11:43:00                                                0.0                                \n",
+       "2015-10-21 11:44:00                                                0.0                                \n",
+       "2015-10-21 11:45:00                                                0.0                                \n",
+       "2015-10-21 11:46:00                                                0.0                                \n",
+       "2015-10-21 11:47:00                                                0.0                                \n",
+       "2015-10-21 11:48:00                                                0.0                                \n",
+       "2015-10-21 11:49:00                                                0.0                                \n",
+       "2015-10-21 11:50:00                                                0.0                                \n",
+       "2015-10-21 11:51:00                                                0.0                                \n",
+       "2015-10-21 11:52:00                                                0.0                                \n",
+       "2015-10-21 11:53:00                                                0.0                                \n",
+       "2015-10-21 11:54:00                                                0.0                                \n",
        "\n",
        "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0  \\\n",
        "2015-10-21 05:09:00                                                0.0                              \n",
        "2015-10-21 05:10:00                                                0.0                              \n",
        "2015-10-21 05:11:00                                                0.0                              \n",
        "2015-10-21 05:12:00                                                0.0                              \n",
-       "2015-10-21 06:02:00                                                0.0                              \n",
        "2015-10-21 06:03:00                                                0.0                              \n",
        "2015-10-21 06:04:00                                                0.0                              \n",
        "2015-10-21 06:05:00                                                0.0                              \n",
-       "2015-10-21 06:06:00                                                0.0                              \n",
-       "2015-10-21 06:07:00                                                0.0                              \n",
-       "2015-10-21 06:08:00                                                0.0                              \n",
-       "2015-10-21 06:09:00                                                0.0                              \n",
+       "2015-10-21 07:01:00                                                0.0                              \n",
+       "2015-10-21 07:02:00                                                0.0                              \n",
+       "2015-10-21 10:35:00                                                0.0                              \n",
+       "2015-10-21 11:19:00                                                0.0                              \n",
+       "2015-10-21 11:36:00                                                0.0                              \n",
+       "2015-10-21 11:37:00                                                0.0                              \n",
        "2015-10-21 06:10:00                                                7.0                              \n",
-       "2015-10-21 06:11:00                                                0.0                              \n",
-       "2015-10-21 06:12:00                                                0.0                              \n",
-       "2015-10-21 06:13:00                                                0.0                              \n",
-       "2015-10-21 06:14:00                                                0.0                              \n",
-       "2015-10-21 06:16:00                                                0.0                              \n",
-       "2015-10-21 06:17:00                                                0.0                              \n",
-       "2015-10-21 06:18:00                                                0.0                              \n",
-       "2015-10-21 06:19:00                                                0.0                              \n",
-       "2015-10-21 06:20:00                                                0.0                              \n",
-       "2015-10-21 06:21:00                                                0.0                              \n",
-       "2015-10-21 06:24:00                                                0.0                              \n",
-       "2015-10-21 06:25:00                                                0.0                              \n",
-       "2015-10-21 06:26:00                                                0.0                              \n",
        "2015-10-21 06:27:00                                               14.0                              \n",
        "2015-10-21 06:28:00                                               28.0                              \n",
        "2015-10-21 06:29:00                                                7.0                              \n",
-       "2015-10-21 07:01:00                                                0.0                              \n",
-       "...                                                                ...                              \n",
+       "2015-10-21 07:42:00                                                3.0                              \n",
+       "2015-10-21 07:43:00                                                2.0                              \n",
+       "2015-10-21 07:59:00                                               16.0                              \n",
+       "2015-10-21 08:00:00                                                6.0                              \n",
+       "2015-10-21 08:01:00                                               16.0                              \n",
+       "2015-10-21 10:29:00                                                0.0                              \n",
+       "2015-10-21 10:31:00                                                0.0                              \n",
+       "2015-10-21 10:32:00                                                0.0                              \n",
+       "2015-10-21 10:33:00                                                0.0                              \n",
        "2015-10-21 10:34:00                                                0.0                              \n",
-       "2015-10-21 10:35:00                                                0.0                              \n",
        "2015-10-21 10:36:00                                                0.0                              \n",
        "2015-10-21 10:37:00                                                0.0                              \n",
        "2015-10-21 10:38:00                                                0.0                              \n",
@@ -1028,11 +1056,8 @@
        "2015-10-21 10:45:00                                                0.0                              \n",
        "2015-10-21 11:10:00                                                0.0                              \n",
        "2015-10-21 11:11:00                                                0.0                              \n",
-       "2015-10-21 11:19:00                                                0.0                              \n",
        "2015-10-21 11:22:00                                                0.0                              \n",
        "2015-10-21 11:24:00                                                0.0                              \n",
-       "2015-10-21 11:36:00                                                0.0                              \n",
-       "2015-10-21 11:37:00                                                0.0                              \n",
        "2015-10-21 11:39:00                                                0.0                              \n",
        "2015-10-21 11:40:00                                                0.0                              \n",
        "2015-10-21 11:41:00                                                0.0                              \n",
@@ -1050,157 +1075,215 @@
        "2015-10-21 11:53:00                                                0.0                              \n",
        "2015-10-21 11:54:00                                                0.0                              \n",
        "\n",
-       "                     Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)  \\\n",
-       "2015-10-21 05:09:00                                                0.0                                \n",
-       "2015-10-21 05:10:00                                                0.0                                \n",
-       "2015-10-21 05:11:00                                              132.0                                \n",
-       "2015-10-21 05:12:00                                               21.0                                \n",
-       "2015-10-21 06:02:00                                                0.0                                \n",
-       "2015-10-21 06:03:00                                                0.0                                \n",
-       "2015-10-21 06:04:00                                               15.0                                \n",
-       "2015-10-21 06:05:00                                                3.0                                \n",
-       "2015-10-21 06:06:00                                                0.0                                \n",
-       "2015-10-21 06:07:00                                                0.0                                \n",
-       "2015-10-21 06:08:00                                                0.0                                \n",
-       "2015-10-21 06:09:00                                                0.0                                \n",
-       "2015-10-21 06:10:00                                                0.0                                \n",
-       "2015-10-21 06:11:00                                                0.0                                \n",
-       "2015-10-21 06:12:00                                                0.0                                \n",
-       "2015-10-21 06:13:00                                                0.0                                \n",
-       "2015-10-21 06:14:00                                                0.0                                \n",
-       "2015-10-21 06:16:00                                                0.0                                \n",
-       "2015-10-21 06:17:00                                                0.0                                \n",
-       "2015-10-21 06:18:00                                                0.0                                \n",
-       "2015-10-21 06:19:00                                                0.0                                \n",
-       "2015-10-21 06:20:00                                                0.0                                \n",
-       "2015-10-21 06:21:00                                                0.0                                \n",
-       "2015-10-21 06:24:00                                                0.0                                \n",
-       "2015-10-21 06:25:00                                                0.0                                \n",
-       "2015-10-21 06:26:00                                                0.0                                \n",
-       "2015-10-21 06:27:00                                                0.0                                \n",
-       "2015-10-21 06:28:00                                                0.0                                \n",
-       "2015-10-21 06:29:00                                                0.0                                \n",
-       "2015-10-21 07:01:00                                                0.0                                \n",
-       "...                                                                ...                                \n",
-       "2015-10-21 10:34:00                                                0.0                                \n",
-       "2015-10-21 10:35:00                                                0.0                                \n",
-       "2015-10-21 10:36:00                                                0.0                                \n",
-       "2015-10-21 10:37:00                                                0.0                                \n",
-       "2015-10-21 10:38:00                                                0.0                                \n",
-       "2015-10-21 10:44:00                                                0.0                                \n",
-       "2015-10-21 10:45:00                                                0.0                                \n",
-       "2015-10-21 11:10:00                                                0.0                                \n",
-       "2015-10-21 11:11:00                                                0.0                                \n",
-       "2015-10-21 11:19:00                                                0.0                                \n",
-       "2015-10-21 11:22:00                                                0.0                                \n",
-       "2015-10-21 11:24:00                                                0.0                                \n",
-       "2015-10-21 11:36:00                                                0.0                                \n",
-       "2015-10-21 11:37:00                                                0.0                                \n",
-       "2015-10-21 11:39:00                                                0.0                                \n",
-       "2015-10-21 11:40:00                                                0.0                                \n",
-       "2015-10-21 11:41:00                                                0.0                                \n",
-       "2015-10-21 11:42:00                                                0.0                                \n",
-       "2015-10-21 11:43:00                                                0.0                                \n",
-       "2015-10-21 11:44:00                                                0.0                                \n",
-       "2015-10-21 11:45:00                                                0.0                                \n",
-       "2015-10-21 11:46:00                                                0.0                                \n",
-       "2015-10-21 11:47:00                                                0.0                                \n",
-       "2015-10-21 11:48:00                                                0.0                                \n",
-       "2015-10-21 11:49:00                                                0.0                                \n",
-       "2015-10-21 11:50:00                                                0.0                                \n",
-       "2015-10-21 11:51:00                                                0.0                                \n",
-       "2015-10-21 11:52:00                                                0.0                                \n",
-       "2015-10-21 11:53:00                                                0.0                                \n",
-       "2015-10-21 11:54:00                                                0.0                                \n",
+       "                     Wget/1.16.1 (linux-gnu)  \\\n",
+       "2015-10-21 05:09:00                      0.0   \n",
+       "2015-10-21 05:10:00                      0.0   \n",
+       "2015-10-21 05:11:00                      0.0   \n",
+       "2015-10-21 05:12:00                      0.0   \n",
+       "2015-10-21 06:03:00                      0.0   \n",
+       "2015-10-21 06:04:00                      0.0   \n",
+       "2015-10-21 06:05:00                      0.0   \n",
+       "2015-10-21 07:01:00                      0.0   \n",
+       "2015-10-21 07:02:00                      0.0   \n",
+       "2015-10-21 10:35:00                      0.0   \n",
+       "2015-10-21 11:19:00                      0.0   \n",
+       "2015-10-21 11:36:00                      0.0   \n",
+       "2015-10-21 11:37:00                      0.0   \n",
+       "2015-10-21 06:10:00                      0.0   \n",
+       "2015-10-21 06:27:00                      0.0   \n",
+       "2015-10-21 06:28:00                      0.0   \n",
+       "2015-10-21 06:29:00                      0.0   \n",
+       "2015-10-21 07:42:00                      1.0   \n",
+       "2015-10-21 07:43:00                      0.0   \n",
+       "2015-10-21 07:59:00                      0.0   \n",
+       "2015-10-21 08:00:00                      0.0   \n",
+       "2015-10-21 08:01:00                      0.0   \n",
+       "2015-10-21 10:29:00                      0.0   \n",
+       "2015-10-21 10:31:00                      0.0   \n",
+       "2015-10-21 10:32:00                      0.0   \n",
+       "2015-10-21 10:33:00                      0.0   \n",
+       "2015-10-21 10:34:00                      0.0   \n",
+       "2015-10-21 10:36:00                      0.0   \n",
+       "2015-10-21 10:37:00                      0.0   \n",
+       "2015-10-21 10:38:00                      0.0   \n",
+       "2015-10-21 10:44:00                      0.0   \n",
+       "2015-10-21 10:45:00                      0.0   \n",
+       "2015-10-21 11:10:00                      0.0   \n",
+       "2015-10-21 11:11:00                      0.0   \n",
+       "2015-10-21 11:22:00                      0.0   \n",
+       "2015-10-21 11:24:00                      0.0   \n",
+       "2015-10-21 11:39:00                      0.0   \n",
+       "2015-10-21 11:40:00                      0.0   \n",
+       "2015-10-21 11:41:00                      0.0   \n",
+       "2015-10-21 11:42:00                      0.0   \n",
+       "2015-10-21 11:43:00                      0.0   \n",
+       "2015-10-21 11:44:00                      0.0   \n",
+       "2015-10-21 11:45:00                      0.0   \n",
+       "2015-10-21 11:46:00                      0.0   \n",
+       "2015-10-21 11:47:00                      0.0   \n",
+       "2015-10-21 11:48:00                      0.0   \n",
+       "2015-10-21 11:49:00                      0.0   \n",
+       "2015-10-21 11:50:00                      0.0   \n",
+       "2015-10-21 11:51:00                      0.0   \n",
+       "2015-10-21 11:52:00                      0.0   \n",
+       "2015-10-21 11:53:00                      0.0   \n",
+       "2015-10-21 11:54:00                      0.0   \n",
        "\n",
-       "                     Wget/1.16.1 (linux-gnu)  \n",
-       "2015-10-21 05:09:00                      0.0  \n",
-       "2015-10-21 05:10:00                      0.0  \n",
-       "2015-10-21 05:11:00                      0.0  \n",
-       "2015-10-21 05:12:00                      0.0  \n",
-       "2015-10-21 06:02:00                      0.0  \n",
-       "2015-10-21 06:03:00                      0.0  \n",
-       "2015-10-21 06:04:00                      0.0  \n",
-       "2015-10-21 06:05:00                      0.0  \n",
-       "2015-10-21 06:06:00                      0.0  \n",
-       "2015-10-21 06:07:00                      0.0  \n",
-       "2015-10-21 06:08:00                      0.0  \n",
-       "2015-10-21 06:09:00                      0.0  \n",
-       "2015-10-21 06:10:00                      0.0  \n",
-       "2015-10-21 06:11:00                      0.0  \n",
-       "2015-10-21 06:12:00                      0.0  \n",
-       "2015-10-21 06:13:00                      0.0  \n",
-       "2015-10-21 06:14:00                      0.0  \n",
-       "2015-10-21 06:16:00                      0.0  \n",
-       "2015-10-21 06:17:00                      0.0  \n",
-       "2015-10-21 06:18:00                      0.0  \n",
-       "2015-10-21 06:19:00                      0.0  \n",
-       "2015-10-21 06:20:00                      0.0  \n",
-       "2015-10-21 06:21:00                      0.0  \n",
-       "2015-10-21 06:24:00                      0.0  \n",
-       "2015-10-21 06:25:00                      0.0  \n",
-       "2015-10-21 06:26:00                      0.0  \n",
-       "2015-10-21 06:27:00                      0.0  \n",
-       "2015-10-21 06:28:00                      0.0  \n",
-       "2015-10-21 06:29:00                      0.0  \n",
-       "2015-10-21 07:01:00                      0.0  \n",
-       "...                                      ...  \n",
-       "2015-10-21 10:34:00                      0.0  \n",
-       "2015-10-21 10:35:00                      0.0  \n",
-       "2015-10-21 10:36:00                      0.0  \n",
-       "2015-10-21 10:37:00                      0.0  \n",
-       "2015-10-21 10:38:00                      0.0  \n",
-       "2015-10-21 10:44:00                      0.0  \n",
-       "2015-10-21 10:45:00                      0.0  \n",
-       "2015-10-21 11:10:00                      0.0  \n",
-       "2015-10-21 11:11:00                      0.0  \n",
-       "2015-10-21 11:19:00                      0.0  \n",
-       "2015-10-21 11:22:00                      0.0  \n",
-       "2015-10-21 11:24:00                      0.0  \n",
-       "2015-10-21 11:36:00                      0.0  \n",
-       "2015-10-21 11:37:00                      0.0  \n",
-       "2015-10-21 11:39:00                      0.0  \n",
-       "2015-10-21 11:40:00                      0.0  \n",
-       "2015-10-21 11:41:00                      0.0  \n",
-       "2015-10-21 11:42:00                      0.0  \n",
-       "2015-10-21 11:43:00                      0.0  \n",
-       "2015-10-21 11:44:00                      0.0  \n",
-       "2015-10-21 11:45:00                      0.0  \n",
-       "2015-10-21 11:46:00                      0.0  \n",
-       "2015-10-21 11:47:00                      0.0  \n",
-       "2015-10-21 11:48:00                      0.0  \n",
-       "2015-10-21 11:49:00                      0.0  \n",
-       "2015-10-21 11:50:00                      0.0  \n",
-       "2015-10-21 11:51:00                      0.0  \n",
-       "2015-10-21 11:52:00                      0.0  \n",
-       "2015-10-21 11:53:00                      0.0  \n",
-       "2015-10-21 11:54:00                      0.0  \n",
+       "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0  \\\n",
+       "2015-10-21 05:09:00                                                0.0                              \n",
+       "2015-10-21 05:10:00                                                0.0                              \n",
+       "2015-10-21 05:11:00                                                0.0                              \n",
+       "2015-10-21 05:12:00                                                0.0                              \n",
+       "2015-10-21 06:03:00                                                0.0                              \n",
+       "2015-10-21 06:04:00                                                0.0                              \n",
+       "2015-10-21 06:05:00                                                0.0                              \n",
+       "2015-10-21 07:01:00                                                0.0                              \n",
+       "2015-10-21 07:02:00                                                0.0                              \n",
+       "2015-10-21 10:35:00                                                0.0                              \n",
+       "2015-10-21 11:19:00                                                0.0                              \n",
+       "2015-10-21 11:36:00                                                0.0                              \n",
+       "2015-10-21 11:37:00                                                0.0                              \n",
+       "2015-10-21 06:10:00                                                0.0                              \n",
+       "2015-10-21 06:27:00                                                0.0                              \n",
+       "2015-10-21 06:28:00                                                0.0                              \n",
+       "2015-10-21 06:29:00                                                0.0                              \n",
+       "2015-10-21 07:42:00                                                0.0                              \n",
+       "2015-10-21 07:43:00                                                0.0                              \n",
+       "2015-10-21 07:59:00                                                0.0                              \n",
+       "2015-10-21 08:00:00                                                0.0                              \n",
+       "2015-10-21 08:01:00                                                0.0                              \n",
+       "2015-10-21 10:29:00                                                8.0                              \n",
+       "2015-10-21 10:31:00                                                2.0                              \n",
+       "2015-10-21 10:32:00                                                2.0                              \n",
+       "2015-10-21 10:33:00                                                2.0                              \n",
+       "2015-10-21 10:34:00                                                1.0                              \n",
+       "2015-10-21 10:36:00                                               44.0                              \n",
+       "2015-10-21 10:37:00                                               43.0                              \n",
+       "2015-10-21 10:38:00                                                2.0                              \n",
+       "2015-10-21 10:44:00                                               38.0                              \n",
+       "2015-10-21 10:45:00                                               33.0                              \n",
+       "2015-10-21 11:10:00                                               25.0                              \n",
+       "2015-10-21 11:11:00                                               11.0                              \n",
+       "2015-10-21 11:22:00                                                3.0                              \n",
+       "2015-10-21 11:24:00                                                3.0                              \n",
+       "2015-10-21 11:39:00                                               18.0                              \n",
+       "2015-10-21 11:40:00                                               12.0                              \n",
+       "2015-10-21 11:41:00                                                8.0                              \n",
+       "2015-10-21 11:42:00                                                6.0                              \n",
+       "2015-10-21 11:43:00                                                6.0                              \n",
+       "2015-10-21 11:44:00                                                5.0                              \n",
+       "2015-10-21 11:45:00                                                6.0                              \n",
+       "2015-10-21 11:46:00                                                6.0                              \n",
+       "2015-10-21 11:47:00                                                5.0                              \n",
+       "2015-10-21 11:48:00                                                6.0                              \n",
+       "2015-10-21 11:49:00                                                6.0                              \n",
+       "2015-10-21 11:50:00                                                6.0                              \n",
+       "2015-10-21 11:51:00                                                5.0                              \n",
+       "2015-10-21 11:52:00                                                6.0                              \n",
+       "2015-10-21 11:53:00                                                5.0                              \n",
+       "2015-10-21 11:54:00                                                4.0                              \n",
        "\n",
-       "[70 rows x 7 columns]"
+       "                     Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0  \n",
+       "2015-10-21 05:09:00                                                0.0                           \n",
+       "2015-10-21 05:10:00                                                0.0                           \n",
+       "2015-10-21 05:11:00                                                0.0                           \n",
+       "2015-10-21 05:12:00                                                0.0                           \n",
+       "2015-10-21 06:03:00                                                0.0                           \n",
+       "2015-10-21 06:04:00                                                0.0                           \n",
+       "2015-10-21 06:05:00                                                0.0                           \n",
+       "2015-10-21 07:01:00                                                0.0                           \n",
+       "2015-10-21 07:02:00                                                0.0                           \n",
+       "2015-10-21 10:35:00                                                0.0                           \n",
+       "2015-10-21 11:19:00                                                0.0                           \n",
+       "2015-10-21 11:36:00                                                0.0                           \n",
+       "2015-10-21 11:37:00                                                0.0                           \n",
+       "2015-10-21 06:10:00                                                0.0                           \n",
+       "2015-10-21 06:27:00                                                0.0                           \n",
+       "2015-10-21 06:28:00                                                0.0                           \n",
+       "2015-10-21 06:29:00                                                0.0                           \n",
+       "2015-10-21 07:42:00                                                0.0                           \n",
+       "2015-10-21 07:43:00                                                0.0                           \n",
+       "2015-10-21 07:59:00                                                0.0                           \n",
+       "2015-10-21 08:00:00                                                0.0                           \n",
+       "2015-10-21 08:01:00                                                0.0                           \n",
+       "2015-10-21 10:29:00                                                0.0                           \n",
+       "2015-10-21 10:31:00                                                0.0                           \n",
+       "2015-10-21 10:32:00                                                0.0                           \n",
+       "2015-10-21 10:33:00                                                0.0                           \n",
+       "2015-10-21 10:34:00                                                0.0                           \n",
+       "2015-10-21 10:36:00                                                0.0                           \n",
+       "2015-10-21 10:37:00                                                0.0                           \n",
+       "2015-10-21 10:38:00                                                0.0                           \n",
+       "2015-10-21 10:44:00                                                0.0                           \n",
+       "2015-10-21 10:45:00                                                0.0                           \n",
+       "2015-10-21 11:10:00                                                0.0                           \n",
+       "2015-10-21 11:11:00                                                0.0                           \n",
+       "2015-10-21 11:22:00                                                0.0                           \n",
+       "2015-10-21 11:24:00                                                0.0                           \n",
+       "2015-10-21 11:39:00                                                0.0                           \n",
+       "2015-10-21 11:40:00                                                0.0                           \n",
+       "2015-10-21 11:41:00                                                0.0                           \n",
+       "2015-10-21 11:42:00                                                0.0                           \n",
+       "2015-10-21 11:43:00                                                0.0                           \n",
+       "2015-10-21 11:44:00                                                0.0                           \n",
+       "2015-10-21 11:45:00                                                0.0                           \n",
+       "2015-10-21 11:46:00                                                0.0                           \n",
+       "2015-10-21 11:47:00                                                0.0                           \n",
+       "2015-10-21 11:48:00                                                0.0                           \n",
+       "2015-10-21 11:49:00                                                0.0                           \n",
+       "2015-10-21 11:50:00                                                0.0                           \n",
+       "2015-10-21 11:51:00                                                0.0                           \n",
+       "2015-10-21 11:52:00                                                0.0                           \n",
+       "2015-10-21 11:53:00                                               12.0                           \n",
+       "2015-10-21 11:54:00                                                0.0                           "
       ]
      },
-     "execution_count": 4,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df = pd.DataFrame.from_dict(user_agent_analysis,orient='columns').fillna(0)\n",
-    "df"
+    "user_agent_analysis2 = user_agent_analysis\n",
+    "print(list(user_agent_analysis2.keys()))\n",
+    "high_volume_user_agents = [\n",
+    "    \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36\"\n",
+    "]\n",
+    "for ua in high_volume_user_agents:\n",
+    "    if ua in user_agent_analysis2.keys():\n",
+    "        del user_agent_analysis2[ua]\n",
+    "df2 = pd.DataFrame.from_dict(user_agent_analysis2,orient='columns').fillna(0)\n",
+    "df2"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 7,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.683376Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.683322Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.804488Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.804055Z"
+    },
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
-      "image/png": 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6IHB3KgMtIiIiNUCpEZfNZlsHpJ+xeDDwVsHPbwFDCi1faLPZ8m022wFgH3CF1WqNBerZ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88+Oyj33xuf+tSn+MlPfnLIV7GDzYvHP2PGjEP6ZKFv7wPIzxMej8cxDINHHnmEhx9+uNcV3qNJ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\nQmCC\n",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x10d253c10>"
+       "<Axes: >"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x900 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -1208,1718 +1291,205 @@
     }
    ],
    "source": [
-    "#df.plot(figsize=(12,9))\n",
-    "\n",
-    "ax = df.plot(rot=90,figsize=(12,9))"
+    "df2.plot(rot=90,figsize=(12,9))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 8,
    "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "['Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0', 'Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)', '-', 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36', 'Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0', 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0', 'Wget/1.16.1 (linux-gnu)']\n"
-     ]
+    "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.805540Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.805477Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.817046Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.816756Z"
     },
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>-</th>\n",
-       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0</th>\n",
-       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0</th>\n",
-       "      <th>Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0</th>\n",
-       "      <th>Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)</th>\n",
-       "      <th>Wget/1.16.1 (linux-gnu)</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:09:00</th>\n",
-       "      <td>29.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:10:00</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:11:00</th>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>132.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:12:00</th>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:03:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:04:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>15.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:05:00</th>\n",
-       "      <td>19.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:10:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:27:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:28:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:29:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 07:01:00</th>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 07:02:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 07:42:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 07:43:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 07:59:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 08:00:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 08:01:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>16.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:29:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:31:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:32:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:33:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:34:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:35:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:36:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>44.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:37:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>43.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:38:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:44:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>38.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:45:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>33.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:10:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>25.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:11:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:19:00</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:22:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:24:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:36:00</th>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:37:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:39:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:40:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:41:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:42:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:43:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:44:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:45:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:46:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:47:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:48:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:49:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:50:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:51:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:52:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:53:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:54:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table>\n",
-       "</div>"
-      ],
-      "text/plain": [
-       "                        -  \\\n",
-       "2015-10-21 05:09:00  29.0   \n",
-       "2015-10-21 05:10:00   2.0   \n",
-       "2015-10-21 05:11:00  21.0   \n",
-       "2015-10-21 05:12:00   7.0   \n",
-       "2015-10-21 06:03:00   1.0   \n",
-       "2015-10-21 06:04:00   1.0   \n",
-       "2015-10-21 06:05:00  19.0   \n",
-       "2015-10-21 06:10:00   0.0   \n",
-       "2015-10-21 06:27:00   0.0   \n",
-       "2015-10-21 06:28:00   0.0   \n",
-       "2015-10-21 06:29:00   0.0   \n",
-       "2015-10-21 07:01:00  12.0   \n",
-       "2015-10-21 07:02:00   1.0   \n",
-       "2015-10-21 07:42:00   0.0   \n",
-       "2015-10-21 07:43:00   0.0   \n",
-       "2015-10-21 07:59:00   0.0   \n",
-       "2015-10-21 08:00:00   0.0   \n",
-       "2015-10-21 08:01:00   0.0   \n",
-       "2015-10-21 10:29:00   0.0   \n",
-       "2015-10-21 10:31:00   0.0   \n",
-       "2015-10-21 10:32:00   0.0   \n",
-       "2015-10-21 10:33:00   0.0   \n",
-       "2015-10-21 10:34:00   0.0   \n",
-       "2015-10-21 10:35:00   1.0   \n",
-       "2015-10-21 10:36:00   0.0   \n",
-       "2015-10-21 10:37:00   0.0   \n",
-       "2015-10-21 10:38:00   0.0   \n",
-       "2015-10-21 10:44:00   0.0   \n",
-       "2015-10-21 10:45:00   0.0   \n",
-       "2015-10-21 11:10:00   0.0   \n",
-       "2015-10-21 11:11:00   0.0   \n",
-       "2015-10-21 11:19:00   2.0   \n",
-       "2015-10-21 11:22:00   0.0   \n",
-       "2015-10-21 11:24:00   0.0   \n",
-       "2015-10-21 11:36:00   6.0   \n",
-       "2015-10-21 11:37:00   1.0   \n",
-       "2015-10-21 11:39:00   0.0   \n",
-       "2015-10-21 11:40:00   0.0   \n",
-       "2015-10-21 11:41:00   0.0   \n",
-       "2015-10-21 11:42:00   0.0   \n",
-       "2015-10-21 11:43:00   0.0   \n",
-       "2015-10-21 11:44:00   0.0   \n",
-       "2015-10-21 11:45:00   0.0   \n",
-       "2015-10-21 11:46:00   0.0   \n",
-       "2015-10-21 11:47:00   0.0   \n",
-       "2015-10-21 11:48:00   0.0   \n",
-       "2015-10-21 11:49:00   0.0   \n",
-       "2015-10-21 11:50:00   0.0   \n",
-       "2015-10-21 11:51:00   0.0   \n",
-       "2015-10-21 11:52:00   0.0   \n",
-       "2015-10-21 11:53:00   0.0   \n",
-       "2015-10-21 11:54:00   0.0   \n",
-       "\n",
-       "                     Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0  \\\n",
-       "2015-10-21 05:09:00                                                0.0                            \n",
-       "2015-10-21 05:10:00                                                0.0                            \n",
-       "2015-10-21 05:11:00                                                0.0                            \n",
-       "2015-10-21 05:12:00                                                0.0                            \n",
-       "2015-10-21 06:03:00                                                0.0                            \n",
-       "2015-10-21 06:04:00                                                0.0                            \n",
-       "2015-10-21 06:05:00                                                0.0                            \n",
-       "2015-10-21 06:10:00                                                0.0                            \n",
-       "2015-10-21 06:27:00                                                0.0                            \n",
-       "2015-10-21 06:28:00                                                0.0                            \n",
-       "2015-10-21 06:29:00                                                0.0                            \n",
-       "2015-10-21 07:01:00                                                0.0                            \n",
-       "2015-10-21 07:02:00                                                0.0                            \n",
-       "2015-10-21 07:42:00                                                0.0                            \n",
-       "2015-10-21 07:43:00                                                0.0                            \n",
-       "2015-10-21 07:59:00                                                0.0                            \n",
-       "2015-10-21 08:00:00                                                0.0                            \n",
-       "2015-10-21 08:01:00                                                0.0                            \n",
-       "2015-10-21 10:29:00                                                0.0                            \n",
-       "2015-10-21 10:31:00                                                0.0                            \n",
-       "2015-10-21 10:32:00                                                0.0                            \n",
-       "2015-10-21 10:33:00                                                0.0                            \n",
-       "2015-10-21 10:34:00                                                0.0                            \n",
-       "2015-10-21 10:35:00                                                0.0                            \n",
-       "2015-10-21 10:36:00                                                0.0                            \n",
-       "2015-10-21 10:37:00                                                0.0                            \n",
-       "2015-10-21 10:38:00                                                0.0                            \n",
-       "2015-10-21 10:44:00                                                0.0                            \n",
-       "2015-10-21 10:45:00                                                0.0                            \n",
-       "2015-10-21 11:10:00                                                0.0                            \n",
-       "2015-10-21 11:11:00                                                0.0                            \n",
-       "2015-10-21 11:19:00                                                0.0                            \n",
-       "2015-10-21 11:22:00                                                0.0                            \n",
-       "2015-10-21 11:24:00                                                0.0                            \n",
-       "2015-10-21 11:36:00                                                0.0                            \n",
-       "2015-10-21 11:37:00                                                0.0                            \n",
-       "2015-10-21 11:39:00                                                0.0                            \n",
-       "2015-10-21 11:40:00                                                0.0                            \n",
-       "2015-10-21 11:41:00                                                0.0                            \n",
-       "2015-10-21 11:42:00                                                0.0                            \n",
-       "2015-10-21 11:43:00                                                0.0                            \n",
-       "2015-10-21 11:44:00                                                0.0                            \n",
-       "2015-10-21 11:45:00                                                0.0                            \n",
-       "2015-10-21 11:46:00                                                0.0                            \n",
-       "2015-10-21 11:47:00                                                0.0                            \n",
-       "2015-10-21 11:48:00                                                0.0                            \n",
-       "2015-10-21 11:49:00                                                0.0                            \n",
-       "2015-10-21 11:50:00                                                0.0                            \n",
-       "2015-10-21 11:51:00                                                0.0                            \n",
-       "2015-10-21 11:52:00                                                0.0                            \n",
-       "2015-10-21 11:53:00                                               12.0                            \n",
-       "2015-10-21 11:54:00                                                0.0                            \n",
-       "\n",
-       "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0  \\\n",
-       "2015-10-21 05:09:00                                                0.0                              \n",
-       "2015-10-21 05:10:00                                                0.0                              \n",
-       "2015-10-21 05:11:00                                                0.0                              \n",
-       "2015-10-21 05:12:00                                                0.0                              \n",
-       "2015-10-21 06:03:00                                                0.0                              \n",
-       "2015-10-21 06:04:00                                                0.0                              \n",
-       "2015-10-21 06:05:00                                                0.0                              \n",
-       "2015-10-21 06:10:00                                                0.0                              \n",
-       "2015-10-21 06:27:00                                                0.0                              \n",
-       "2015-10-21 06:28:00                                                0.0                              \n",
-       "2015-10-21 06:29:00                                                0.0                              \n",
-       "2015-10-21 07:01:00                                                0.0                              \n",
-       "2015-10-21 07:02:00                                                0.0                              \n",
-       "2015-10-21 07:42:00                                                0.0                              \n",
-       "2015-10-21 07:43:00                                                0.0                              \n",
-       "2015-10-21 07:59:00                                                0.0                              \n",
-       "2015-10-21 08:00:00                                                0.0                              \n",
-       "2015-10-21 08:01:00                                                0.0                              \n",
-       "2015-10-21 10:29:00                                                8.0                              \n",
-       "2015-10-21 10:31:00                                                2.0                              \n",
-       "2015-10-21 10:32:00                                                2.0                              \n",
-       "2015-10-21 10:33:00                                                2.0                              \n",
-       "2015-10-21 10:34:00                                                1.0                              \n",
-       "2015-10-21 10:35:00                                                0.0                              \n",
-       "2015-10-21 10:36:00                                               44.0                              \n",
-       "2015-10-21 10:37:00                                               43.0                              \n",
-       "2015-10-21 10:38:00                                                2.0                              \n",
-       "2015-10-21 10:44:00                                               38.0                              \n",
-       "2015-10-21 10:45:00                                               33.0                              \n",
-       "2015-10-21 11:10:00                                               25.0                              \n",
-       "2015-10-21 11:11:00                                               11.0                              \n",
-       "2015-10-21 11:19:00                                                0.0                              \n",
-       "2015-10-21 11:22:00                                                3.0                              \n",
-       "2015-10-21 11:24:00                                                3.0                              \n",
-       "2015-10-21 11:36:00                                                0.0                              \n",
-       "2015-10-21 11:37:00                                                0.0                              \n",
-       "2015-10-21 11:39:00                                               18.0                              \n",
-       "2015-10-21 11:40:00                                               12.0                              \n",
-       "2015-10-21 11:41:00                                                8.0                              \n",
-       "2015-10-21 11:42:00                                                6.0                              \n",
-       "2015-10-21 11:43:00                                                6.0                              \n",
-       "2015-10-21 11:44:00                                                5.0                              \n",
-       "2015-10-21 11:45:00                                                6.0                              \n",
-       "2015-10-21 11:46:00                                                6.0                              \n",
-       "2015-10-21 11:47:00                                                5.0                              \n",
-       "2015-10-21 11:48:00                                                6.0                              \n",
-       "2015-10-21 11:49:00                                                6.0                              \n",
-       "2015-10-21 11:50:00                                                6.0                              \n",
-       "2015-10-21 11:51:00                                                5.0                              \n",
-       "2015-10-21 11:52:00                                                6.0                              \n",
-       "2015-10-21 11:53:00                                                5.0                              \n",
-       "2015-10-21 11:54:00                                                4.0                              \n",
-       "\n",
-       "                     Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0  \\\n",
-       "2015-10-21 05:09:00                                                0.0                              \n",
-       "2015-10-21 05:10:00                                                0.0                              \n",
-       "2015-10-21 05:11:00                                                0.0                              \n",
-       "2015-10-21 05:12:00                                                0.0                              \n",
-       "2015-10-21 06:03:00                                                0.0                              \n",
-       "2015-10-21 06:04:00                                                0.0                              \n",
-       "2015-10-21 06:05:00                                                0.0                              \n",
-       "2015-10-21 06:10:00                                                7.0                              \n",
-       "2015-10-21 06:27:00                                               14.0                              \n",
-       "2015-10-21 06:28:00                                               28.0                              \n",
-       "2015-10-21 06:29:00                                                7.0                              \n",
-       "2015-10-21 07:01:00                                                0.0                              \n",
-       "2015-10-21 07:02:00                                                0.0                              \n",
-       "2015-10-21 07:42:00                                                3.0                              \n",
-       "2015-10-21 07:43:00                                                2.0                              \n",
-       "2015-10-21 07:59:00                                               16.0                              \n",
-       "2015-10-21 08:00:00                                                6.0                              \n",
-       "2015-10-21 08:01:00                                               16.0                              \n",
-       "2015-10-21 10:29:00                                                0.0                              \n",
-       "2015-10-21 10:31:00                                                0.0                              \n",
-       "2015-10-21 10:32:00                                                0.0                              \n",
-       "2015-10-21 10:33:00                                                0.0                              \n",
-       "2015-10-21 10:34:00                                                0.0                              \n",
-       "2015-10-21 10:35:00                                                0.0                              \n",
-       "2015-10-21 10:36:00                                                0.0                              \n",
-       "2015-10-21 10:37:00                                                0.0                              \n",
-       "2015-10-21 10:38:00                                                0.0                              \n",
-       "2015-10-21 10:44:00                                                0.0                              \n",
-       "2015-10-21 10:45:00                                                0.0                              \n",
-       "2015-10-21 11:10:00                                                0.0                              \n",
-       "2015-10-21 11:11:00                                                0.0                              \n",
-       "2015-10-21 11:19:00                                                0.0                              \n",
-       "2015-10-21 11:22:00                                                0.0                              \n",
-       "2015-10-21 11:24:00                                                0.0                              \n",
-       "2015-10-21 11:36:00                                                0.0                              \n",
-       "2015-10-21 11:37:00                                                0.0                              \n",
-       "2015-10-21 11:39:00                                                0.0                              \n",
-       "2015-10-21 11:40:00                                                0.0                              \n",
-       "2015-10-21 11:41:00                                                0.0                              \n",
-       "2015-10-21 11:42:00                                                0.0                              \n",
-       "2015-10-21 11:43:00                                                0.0                              \n",
-       "2015-10-21 11:44:00                                                0.0                              \n",
-       "2015-10-21 11:45:00                                                0.0                              \n",
-       "2015-10-21 11:46:00                                                0.0                              \n",
-       "2015-10-21 11:47:00                                                0.0                              \n",
-       "2015-10-21 11:48:00                                                0.0                              \n",
-       "2015-10-21 11:49:00                                                0.0                              \n",
-       "2015-10-21 11:50:00                                                0.0                              \n",
-       "2015-10-21 11:51:00                                                0.0                              \n",
-       "2015-10-21 11:52:00                                                0.0                              \n",
-       "2015-10-21 11:53:00                                                0.0                              \n",
-       "2015-10-21 11:54:00                                                0.0                              \n",
-       "\n",
-       "                     Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)  \\\n",
-       "2015-10-21 05:09:00                                                0.0                                \n",
-       "2015-10-21 05:10:00                                                0.0                                \n",
-       "2015-10-21 05:11:00                                              132.0                                \n",
-       "2015-10-21 05:12:00                                               21.0                                \n",
-       "2015-10-21 06:03:00                                                0.0                                \n",
-       "2015-10-21 06:04:00                                               15.0                                \n",
-       "2015-10-21 06:05:00                                                3.0                                \n",
-       "2015-10-21 06:10:00                                                0.0                                \n",
-       "2015-10-21 06:27:00                                                0.0                                \n",
-       "2015-10-21 06:28:00                                                0.0                                \n",
-       "2015-10-21 06:29:00                                                0.0                                \n",
-       "2015-10-21 07:01:00                                                0.0                                \n",
-       "2015-10-21 07:02:00                                                0.0                                \n",
-       "2015-10-21 07:42:00                                                0.0                                \n",
-       "2015-10-21 07:43:00                                                0.0                                \n",
-       "2015-10-21 07:59:00                                                0.0                                \n",
-       "2015-10-21 08:00:00                                                0.0                                \n",
-       "2015-10-21 08:01:00                                                0.0                                \n",
-       "2015-10-21 10:29:00                                                0.0                                \n",
-       "2015-10-21 10:31:00                                                0.0                                \n",
-       "2015-10-21 10:32:00                                                0.0                                \n",
-       "2015-10-21 10:33:00                                                0.0                                \n",
-       "2015-10-21 10:34:00                                                0.0                                \n",
-       "2015-10-21 10:35:00                                                0.0                                \n",
-       "2015-10-21 10:36:00                                                0.0                                \n",
-       "2015-10-21 10:37:00                                                0.0                                \n",
-       "2015-10-21 10:38:00                                                0.0                                \n",
-       "2015-10-21 10:44:00                                                0.0                                \n",
-       "2015-10-21 10:45:00                                                0.0                                \n",
-       "2015-10-21 11:10:00                                                0.0                                \n",
-       "2015-10-21 11:11:00                                                0.0                                \n",
-       "2015-10-21 11:19:00                                                0.0                                \n",
-       "2015-10-21 11:22:00                                                0.0                                \n",
-       "2015-10-21 11:24:00                                                0.0                                \n",
-       "2015-10-21 11:36:00                                                0.0                                \n",
-       "2015-10-21 11:37:00                                                0.0                                \n",
-       "2015-10-21 11:39:00                                                0.0                                \n",
-       "2015-10-21 11:40:00                                                0.0                                \n",
-       "2015-10-21 11:41:00                                                0.0                                \n",
-       "2015-10-21 11:42:00                                                0.0                                \n",
-       "2015-10-21 11:43:00                                                0.0                                \n",
-       "2015-10-21 11:44:00                                                0.0                                \n",
-       "2015-10-21 11:45:00                                                0.0                                \n",
-       "2015-10-21 11:46:00                                                0.0                                \n",
-       "2015-10-21 11:47:00                                                0.0                                \n",
-       "2015-10-21 11:48:00                                                0.0                                \n",
-       "2015-10-21 11:49:00                                                0.0                                \n",
-       "2015-10-21 11:50:00                                                0.0                                \n",
-       "2015-10-21 11:51:00                                                0.0                                \n",
-       "2015-10-21 11:52:00                                                0.0                                \n",
-       "2015-10-21 11:53:00                                                0.0                                \n",
-       "2015-10-21 11:54:00                                                0.0                                \n",
-       "\n",
-       "                     Wget/1.16.1 (linux-gnu)  \n",
-       "2015-10-21 05:09:00                      0.0  \n",
-       "2015-10-21 05:10:00                      0.0  \n",
-       "2015-10-21 05:11:00                      0.0  \n",
-       "2015-10-21 05:12:00                      0.0  \n",
-       "2015-10-21 06:03:00                      0.0  \n",
-       "2015-10-21 06:04:00                      0.0  \n",
-       "2015-10-21 06:05:00                      0.0  \n",
-       "2015-10-21 06:10:00                      0.0  \n",
-       "2015-10-21 06:27:00                      0.0  \n",
-       "2015-10-21 06:28:00                      0.0  \n",
-       "2015-10-21 06:29:00                      0.0  \n",
-       "2015-10-21 07:01:00                      0.0  \n",
-       "2015-10-21 07:02:00                      0.0  \n",
-       "2015-10-21 07:42:00                      1.0  \n",
-       "2015-10-21 07:43:00                      0.0  \n",
-       "2015-10-21 07:59:00                      0.0  \n",
-       "2015-10-21 08:00:00                      0.0  \n",
-       "2015-10-21 08:01:00                      0.0  \n",
-       "2015-10-21 10:29:00                      0.0  \n",
-       "2015-10-21 10:31:00                      0.0  \n",
-       "2015-10-21 10:32:00                      0.0  \n",
-       "2015-10-21 10:33:00                      0.0  \n",
-       "2015-10-21 10:34:00                      0.0  \n",
-       "2015-10-21 10:35:00                      0.0  \n",
-       "2015-10-21 10:36:00                      0.0  \n",
-       "2015-10-21 10:37:00                      0.0  \n",
-       "2015-10-21 10:38:00                      0.0  \n",
-       "2015-10-21 10:44:00                      0.0  \n",
-       "2015-10-21 10:45:00                      0.0  \n",
-       "2015-10-21 11:10:00                      0.0  \n",
-       "2015-10-21 11:11:00                      0.0  \n",
-       "2015-10-21 11:19:00                      0.0  \n",
-       "2015-10-21 11:22:00                      0.0  \n",
-       "2015-10-21 11:24:00                      0.0  \n",
-       "2015-10-21 11:36:00                      0.0  \n",
-       "2015-10-21 11:37:00                      0.0  \n",
-       "2015-10-21 11:39:00                      0.0  \n",
-       "2015-10-21 11:40:00                      0.0  \n",
-       "2015-10-21 11:41:00                      0.0  \n",
-       "2015-10-21 11:42:00                      0.0  \n",
-       "2015-10-21 11:43:00                      0.0  \n",
-       "2015-10-21 11:44:00                      0.0  \n",
-       "2015-10-21 11:45:00                      0.0  \n",
-       "2015-10-21 11:46:00                      0.0  \n",
-       "2015-10-21 11:47:00                      0.0  \n",
-       "2015-10-21 11:48:00                      0.0  \n",
-       "2015-10-21 11:49:00                      0.0  \n",
-       "2015-10-21 11:50:00                      0.0  \n",
-       "2015-10-21 11:51:00                      0.0  \n",
-       "2015-10-21 11:52:00                      0.0  \n",
-       "2015-10-21 11:53:00                      0.0  \n",
-       "2015-10-21 11:54:00                      0.0  "
-      ]
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "user_agent_analysis2 = user_agent_analysis\n",
-    "print(user_agent_analysis2.keys())\n",
-    "high_volume_user_agents = [\n",
-    "    \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36\"\n",
-    "]\n",
-    "for ua in high_volume_user_agents:    \n",
-    "    if ua in user_agent_analysis2.keys():\n",
-    "        del user_agent_analysis2[ua]\n",
-    "df2 = pd.DataFrame.from_dict(user_agent_analysis2,orient='columns').fillna(0)\n",
-    "df2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x110b9ee90>"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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w5HI5qampksL85MkTAgMDOXz4MNWqVWPChAn8/PPPdOnSRa+cmzdvEhYWxrFj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\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110ad7290>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "df2.plot(rot=90,figsize=(12,9))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {
-    "collapsed": true,
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "# Lets analyze status codes\n",
-    "status_code_analysis = {}\n",
-    "status_code_overall = {}\n",
-    "earliest_time = None\n",
-    "latest_time = None\n",
-    "for line in http_data:\n",
-    "    # Extract the timestamp\n",
-    "    timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n",
-    "    # Strip minute, second and microsecond from timestamp\n",
-    "    #timestamp = str(timestamp.replace(minute=0,second=0,microsecond=0))\n",
-    "    timestamp = str(timestamp.replace(second=0,microsecond=0))\n",
-    "    \n",
-    "    # Extract the status code\n",
-    "    status_code = line.split('\\t')[14]\n",
-    "    \n",
-    "    # Update status code analysis variable\n",
-    "    if status_code not in status_code_analysis.keys():\n",
-    "        status_code_analysis[status_code] = {timestamp: 1}\n",
-    "    else:\n",
-    "        if timestamp not in status_code_analysis[status_code].keys():\n",
-    "            status_code_analysis[status_code][timestamp] = 1\n",
-    "        else:\n",
-    "            status_code_analysis[status_code][timestamp] += 1\n",
-    "            \n",
-    "    # Update overall status code count\n",
-    "    if status_code not in status_code_overall.keys():\n",
-    "        status_code_overall[status_code] = 1\n",
-    "    else:\n",
-    "        status_code_overall[status_code] += 1\n",
-    "    \n",
-    "    # Update our earliest and latest time as needed\n",
-    "    if earliest_time is None or timestamp < earliest_time:\n",
-    "        earliest_time = timestamp\n",
-    "    if latest_time is None or timestamp > latest_time:\n",
-    "        latest_time = timestamp"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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jF2MA/BZAlZm1/M7MWhIuxhZKYGAYBgaDQQTknlSCIJCupprl5eXGxYsXNwLA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\nRERUchhwiIiIqOT8H2bAo6lU5KZKAAAAAElFTkSuQmCC\n",
-      "text/plain": [
-       "<matplotlib.figure.Figure at 0x110ffde50>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "# Format data for the plot function\n",
-    "status_label = []\n",
-    "data = []\n",
-    "for code in sorted(status_code_overall.keys()):\n",
-    "    status_label.append(str(code) + \" (\" + str(status_code_overall[code]) + \")\")\n",
-    "    data.append(status_code_overall[code])\n",
-    "\n",
-    "plot.figure(1,figsize=[8,8])\n",
-    "patches, texts = plot.pie(data, shadow=True, startangle=90)\n",
-    "plot.legend(patches, status_label,loc=\"best\")\n",
-    "plot.title('Status Code Distribution')\n",
-    "plot.axis('equal')\n",
-    "plot.tight_layout()\n",
-    "plot.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {
-    "scrolled": false
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe tbody tr th {\n",
-       "        vertical-align: top;\n",
-       "    }\n",
-       "</style>\n",
-       "<table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       "    <tr style=\"text-align: right;\">\n",
-       "      <th></th>\n",
-       "      <th>-</th>\n",
-       "      <th>200</th>\n",
-       "      <th>301</th>\n",
-       "      <th>302</th>\n",
-       "      <th>304</th>\n",
-       "      <th>400</th>\n",
-       "      <th>401</th>\n",
-       "      <th>404</th>\n",
-       "      <th>405</th>\n",
-       "      <th>501</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:09:00</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>10.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:10:00</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:11:00</th>\n",
-       "      <td>8.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>39.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>11.0</td>\n",
-       "      <td>39.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 05:12:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>2.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:02:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:03:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:04:00</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:05:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>376.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>22.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:06:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1196.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:07:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>993.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:08:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1171.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:09:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>1015.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:10:00</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>20.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:11:00</th>\n",
-       "      <td>5.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:12:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:13:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:14:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>63.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:16:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>58.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:17:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>13.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:18:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:19:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:20:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>46.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:21:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:24:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:25:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:26:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:27:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:28:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>28.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 06:29:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>7.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 07:01:00</th>\n",
-       "      <td>4.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>...</th>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "      <td>...</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:34:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:35:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:36:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>24.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>18.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:37:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>9.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>34.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:38:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:44:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>32.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 10:45:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>30.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "# Lets analyze status codes\n",
+    "status_code_analysis = {}\n",
+    "status_code_overall = {}\n",
+    "earliest_time = None\n",
+    "latest_time = None\n",
+    "for line in http_data:\n",
+    "    # Extract the timestamp\n",
+    "    timestamp = datetime.fromtimestamp(float(line.split('\\t')[0]))\n",
+    "    # Strip minute, second and microsecond from timestamp\n",
+    "    #timestamp = str(timestamp.replace(minute=0,second=0,microsecond=0))\n",
+    "    timestamp = str(timestamp.replace(second=0,microsecond=0))\n",
+    "\n",
+    "    # Extract the status code\n",
+    "    status_code = line.split('\\t')[14]\n",
+    "\n",
+    "    # Update status code analysis variable\n",
+    "    if status_code not in status_code_analysis.keys():\n",
+    "        status_code_analysis[status_code] = {timestamp: 1}\n",
+    "    else:\n",
+    "        if timestamp not in status_code_analysis[status_code].keys():\n",
+    "            status_code_analysis[status_code][timestamp] = 1\n",
+    "        else:\n",
+    "            status_code_analysis[status_code][timestamp] += 1\n",
+    "\n",
+    "    # Update overall status code count\n",
+    "    if status_code not in status_code_overall.keys():\n",
+    "        status_code_overall[status_code] = 1\n",
+    "    else:\n",
+    "        status_code_overall[status_code] += 1\n",
+    "\n",
+    "    # Update our earliest and latest time as needed\n",
+    "    if earliest_time is None or timestamp < earliest_time:\n",
+    "        earliest_time = timestamp\n",
+    "    if latest_time is None or timestamp > latest_time:\n",
+    "        latest_time = timestamp"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.818142Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.818084Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.868812Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.868499Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Format data for the plot function\n",
+    "status_label = []\n",
+    "data = []\n",
+    "for code in sorted(status_code_overall.keys()):\n",
+    "    status_label.append(str(code) + \" (\" + str(status_code_overall[code]) + \")\")\n",
+    "    data.append(status_code_overall[code])\n",
+    "\n",
+    "plot.figure(1,figsize=[8,8])\n",
+    "patches, texts = plot.pie(data, shadow=True, startangle=90)\n",
+    "plot.legend(patches, status_label,loc=\"best\")\n",
+    "plot.title('Status Code Distribution')\n",
+    "plot.axis('equal')\n",
+    "plot.tight_layout()\n",
+    "plot.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.869905Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.869841Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.875028Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.874677Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>200</th>\n",
+       "      <th>302</th>\n",
+       "      <th>401</th>\n",
+       "      <th>501</th>\n",
+       "      <th>-</th>\n",
+       "      <th>404</th>\n",
+       "      <th>400</th>\n",
+       "      <th>405</th>\n",
+       "      <th>301</th>\n",
+       "      <th>304</th>\n",
        "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:10:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>21.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 05:09:00</th>\n",
        "      <td>3.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <th>2015-10-21 11:11:00</th>\n",
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-       "      <th>2015-10-21 11:19:00</th>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:22:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "    <tr>\n",
-       "      <th>2015-10-21 11:24:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>1.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:36:00</th>\n",
        "      <td>4.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>2.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:37:00</th>\n",
-       "      <td>1.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:39:00</th>\n",
        "      <td>2.0</td>\n",
-       "      <td>14.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>8.0</td>\n",
        "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:40:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>12.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:41:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 05:11:00</th>\n",
+       "      <td>18.0</td>\n",
+       "      <td>13.0</td>\n",
+       "      <td>11.0</td>\n",
+       "      <td>39.0</td>\n",
        "      <td>8.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:42:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
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-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:43:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:44:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>14.0</td>\n",
+       "      <td>39.0</td>\n",
+       "      <td>11.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:45:00</th>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 06:04:00</th>\n",
+       "      <td>1.0</td>\n",
        "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <td>7.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:46:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 06:05:00</th>\n",
+       "      <td>376.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>22.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:47:00</th>\n",
+       "      <th>2015-10-21 06:06:00</th>\n",
+       "      <td>1196.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2930,37 +1500,37 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:48:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:49:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 05:12:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:50:00</th>\n",
+       "      <th>2015-10-21 10:34:00</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2969,12 +1539,12 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:51:00</th>\n",
+       "      <th>2015-10-21 05:10:00</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>5.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>2.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
@@ -2982,22 +1552,10 @@
        "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:52:00</th>\n",
-       "      <td>0.0</td>\n",
-       "      <td>6.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
+       "      <th>2015-10-21 11:37:00</th>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "    </tr>\n",
-       "    <tr>\n",
-       "      <th>2015-10-21 11:53:00</th>\n",
-       "      <td>2.0</td>\n",
-       "      <td>14.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>1.0</td>\n",
        "      <td>0.0</td>\n",
@@ -3005,16 +1563,15 @@
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
-       "      <td>0.0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2015-10-21 11:54:00</th>\n",
+       "      <th>2015-10-21 10:35:00</th>\n",
        "      <td>0.0</td>\n",
-       "      <td>4.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
        "      <td>0.0</td>\n",
+       "      <td>1.0</td>\n",
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        "      <td>0.0</td>\n",
@@ -3026,131 +1583,18 @@
        "</div>"
       ],
       "text/plain": [
-       "                       -     200  301   302   304   400   401   404   405  \\\n",
-       "2015-10-21 05:09:00  8.0     3.0  0.0   4.0   0.0   0.0   2.0   2.0   0.0   \n",
-       "2015-10-21 05:10:00  2.0     0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 05:11:00  8.0    18.0  0.0  13.0   0.0  39.0  11.0  14.0  11.0   \n",
-       "2015-10-21 05:12:00  0.0     0.0  0.0   0.0   0.0  22.0   0.0   2.0   2.0   \n",
-       "2015-10-21 06:02:00  1.0     0.0  0.0   0.0   0.0   0.0   1.0   2.0   0.0   \n",
-       "2015-10-21 06:03:00  0.0     0.0  0.0   1.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:04:00  4.0     1.0  0.0   6.0   0.0   7.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:05:00  1.0   376.0  0.0   0.0   0.0  22.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:06:00  0.0  1196.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:07:00  0.0   993.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:08:00  0.0  1171.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:09:00  1.0  1015.0  0.0   1.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:10:00  5.0    20.0  0.0   1.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:11:00  5.0    13.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:12:00  0.0     1.0  0.0   0.0   0.0   0.0   0.0   1.0   0.0   \n",
-       "2015-10-21 06:13:00  0.0    13.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:14:00  0.0    63.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:16:00  0.0    58.0  0.0   6.0   0.0   2.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:17:00  0.0    13.0  0.0   0.0   0.0   6.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:18:00  0.0     6.0  0.0   0.0   0.0   3.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:19:00  0.0     2.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:20:00  0.0    46.0  0.0   2.0   0.0   7.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:21:00  0.0     2.0  0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:24:00  0.0     3.0  0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:25:00  0.0     7.0  0.0   0.0   0.0   3.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:26:00  0.0     1.0  0.0   0.0   0.0   1.0   0.0   0.0   0.0   \n",
-       "2015-10-21 06:27:00  0.0    14.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
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-       "2015-10-21 06:29:00  0.0     7.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 07:01:00  4.0     2.0  0.0   4.0   0.0   0.0   0.0   1.0   0.0   \n",
-       "...                  ...     ...  ...   ...   ...   ...   ...   ...   ...   \n",
-       "2015-10-21 10:34:00  0.0     0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 10:35:00  0.0     0.0  0.0   0.0   0.0   0.0   0.0   1.0   0.0   \n",
-       "2015-10-21 10:36:00  0.0    24.0  0.0   0.0  18.0   0.0   2.0   0.0   0.0   \n",
-       "2015-10-21 10:37:00  0.0     9.0  0.0   0.0  34.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 10:38:00  0.0     2.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 10:44:00  0.0    32.0  0.0   6.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 10:45:00  0.0    30.0  0.0   3.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:10:00  0.0    21.0  0.0   3.0   0.0   0.0   1.0   0.0   0.0   \n",
-       "2015-10-21 11:11:00  0.0    10.0  0.0   1.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:19:00  0.0     2.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:22:00  0.0     1.0  0.0   0.0   0.0   0.0   0.0   2.0   0.0   \n",
-       "2015-10-21 11:24:00  0.0     1.0  0.0   0.0   0.0   0.0   0.0   2.0   0.0   \n",
-       "2015-10-21 11:36:00  4.0     0.0  0.0   2.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:37:00  1.0     0.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:39:00  2.0    14.0  0.0   2.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:40:00  0.0    12.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:41:00  0.0     8.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:42:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:43:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:44:00  0.0     5.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:45:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:46:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:47:00  0.0     5.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:48:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:49:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:50:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:51:00  0.0     5.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:52:00  0.0     6.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:53:00  2.0    14.0  0.0   1.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "2015-10-21 11:54:00  0.0     4.0  0.0   0.0   0.0   0.0   0.0   0.0   0.0   \n",
-       "\n",
-       "                      501  \n",
-       "2015-10-21 05:09:00  10.0  \n",
-       "2015-10-21 05:10:00   0.0  \n",
-       "2015-10-21 05:11:00  39.0  \n",
-       "2015-10-21 05:12:00   2.0  \n",
-       "2015-10-21 06:02:00   0.0  \n",
-       "2015-10-21 06:03:00   0.0  \n",
-       "2015-10-21 06:04:00   0.0  \n",
-       "2015-10-21 06:05:00   0.0  \n",
-       "2015-10-21 06:06:00   0.0  \n",
-       "2015-10-21 06:07:00   0.0  \n",
-       "2015-10-21 06:08:00   0.0  \n",
-       "2015-10-21 06:09:00   0.0  \n",
-       "2015-10-21 06:10:00   0.0  \n",
-       "2015-10-21 06:11:00   0.0  \n",
-       "2015-10-21 06:12:00   0.0  \n",
-       "2015-10-21 06:13:00   0.0  \n",
-       "2015-10-21 06:14:00   0.0  \n",
-       "2015-10-21 06:16:00   0.0  \n",
-       "2015-10-21 06:17:00   0.0  \n",
-       "2015-10-21 06:18:00   0.0  \n",
-       "2015-10-21 06:19:00   0.0  \n",
-       "2015-10-21 06:20:00   0.0  \n",
-       "2015-10-21 06:21:00   0.0  \n",
-       "2015-10-21 06:24:00   0.0  \n",
-       "2015-10-21 06:25:00   0.0  \n",
-       "2015-10-21 06:26:00   0.0  \n",
-       "2015-10-21 06:27:00   0.0  \n",
-       "2015-10-21 06:28:00   0.0  \n",
-       "2015-10-21 06:29:00   0.0  \n",
-       "2015-10-21 07:01:00   1.0  \n",
-       "...                   ...  \n",
-       "2015-10-21 10:34:00   1.0  \n",
-       "2015-10-21 10:35:00   0.0  \n",
-       "2015-10-21 10:36:00   0.0  \n",
-       "2015-10-21 10:37:00   0.0  \n",
-       "2015-10-21 10:38:00   0.0  \n",
-       "2015-10-21 10:44:00   0.0  \n",
-       "2015-10-21 10:45:00   0.0  \n",
-       "2015-10-21 11:10:00   0.0  \n",
-       "2015-10-21 11:11:00   0.0  \n",
-       "2015-10-21 11:19:00   0.0  \n",
-       "2015-10-21 11:22:00   0.0  \n",
-       "2015-10-21 11:24:00   0.0  \n",
-       "2015-10-21 11:36:00   0.0  \n",
-       "2015-10-21 11:37:00   0.0  \n",
-       "2015-10-21 11:39:00   0.0  \n",
-       "2015-10-21 11:40:00   0.0  \n",
-       "2015-10-21 11:41:00   0.0  \n",
-       "2015-10-21 11:42:00   0.0  \n",
-       "2015-10-21 11:43:00   0.0  \n",
-       "2015-10-21 11:44:00   0.0  \n",
-       "2015-10-21 11:45:00   0.0  \n",
-       "2015-10-21 11:46:00   0.0  \n",
-       "2015-10-21 11:47:00   0.0  \n",
-       "2015-10-21 11:48:00   0.0  \n",
-       "2015-10-21 11:49:00   0.0  \n",
-       "2015-10-21 11:50:00   0.0  \n",
-       "2015-10-21 11:51:00   0.0  \n",
-       "2015-10-21 11:52:00   0.0  \n",
-       "2015-10-21 11:53:00   0.0  \n",
-       "2015-10-21 11:54:00   0.0  \n",
+       "                        200   302   401   501    -   404   400   405  301  304\n",
+       "2015-10-21 05:09:00     3.0   4.0   2.0  10.0  8.0   2.0   0.0   0.0  0.0  0.0\n",
+       "2015-10-21 05:11:00    18.0  13.0  11.0  39.0  8.0  14.0  39.0  11.0  0.0  0.0\n",
+       "2015-10-21 06:04:00     1.0   6.0   0.0   0.0  4.0   0.0   7.0   0.0  0.0  0.0\n",
+       "2015-10-21 06:05:00   376.0   0.0   0.0   0.0  1.0   0.0  22.0   0.0  0.0  0.0\n",
+       "2015-10-21 06:06:00  1196.0   0.0   0.0   0.0  0.0   0.0   0.0   0.0  0.0  0.0\n",
+       "...                     ...   ...   ...   ...  ...   ...   ...   ...  ...  ...\n",
+       "2015-10-21 05:12:00     0.0   0.0   0.0   2.0  0.0   2.0  22.0   2.0  0.0  0.0\n",
+       "2015-10-21 10:34:00     0.0   0.0   0.0   1.0  0.0   0.0   0.0   0.0  0.0  0.0\n",
+       "2015-10-21 05:10:00     0.0   0.0   0.0   0.0  2.0   0.0   0.0   0.0  0.0  0.0\n",
+       "2015-10-21 11:37:00     0.0   0.0   0.0   0.0  1.0   0.0   0.0   0.0  0.0  0.0\n",
+       "2015-10-21 10:35:00     0.0   0.0   0.0   0.0  0.0   1.0   0.0   0.0  0.0  0.0\n",
        "\n",
        "[70 rows x 10 columns]"
       ]
@@ -3170,13 +1614,19 @@
    "cell_type": "code",
    "execution_count": 11,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.875958Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.875903Z",
+     "iopub.status.idle": "2026-09-05T22:08:54.948330Z",
+     "shell.execute_reply": "2026-09-05T22:08:54.947893Z"
+    },
     "scrolled": false
    },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x104d01990>"
+       "<Axes: >"
       ]
      },
      "execution_count": 11,
@@ -3185,9 +1635,9 @@
     },
     {
      "data": {
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fcfnll8/IWuyBDki5UqUjd/aW9s7r4NRYjePFt58FPT7C7hyHqDStWlj/mScSSpKkmXbo\n0CGOHTsGwBtvvMELL7zAlVdeyfr16xkaGgJgaGiIDRs2AHDs2DHuuOMOPv3pT/NLv/RLM7YuK9AB\nKVVqZ/VAw5tH2S3sPv+W7zteIgMsm3f+yvLC7jwLu3K86iQOSZI0w4aHh3nwwQepVqtUq1Vuu+02\n1q1bx/ve9z7uvfdeNm/ezMqVK9m0aRMAf/Inf8Irr7zCH/zBH/AHf/AHAGzevJmenp6WrssAHZDy\neQJ0c5TdwRNl3t1zyXlfv2+0RM+cPF35t/8PE6sWdTnKTpIkzbirr76abdu2nXV98eLFxHF81vUH\nH3yQBx98cMbXZQtHQErnmMIB0Duv3nZxoUkc+0ZLb9u+0bR6YSd7jpWo1WpTW6gkSVKKGaADUqrU\nztkDfUlHlgVdubcN0LVarR6g3+YBwqbVC+uTOEZOjk1rvZIkSWlkgA5IuXruFg5ojLJ7m9MIjxcr\nnChVJ1SBbh7pbRuHJEmajQzQASmdZw40wNK5HRwsnH9yxkRG2DWtbkzi8EFCSZI0GxmgA1I+Tw80\n1CvQB0+MUT1P3/K+4xMP0Au68yzszjnKTpIkzUoG6ICUKjU6sufe0qVzOxir1jj8xrn7lveNlslm\nTo+8u5DVC7t49agVaEmSNPsYoANSrtTozJ+/Ag3nn8Sxb7TEsnkd5M9xkuG5OIlDkiTNtFOnTnHr\nrbfy/ve/n5tuuolHH30UgCNHjjAwMMDatWsZGBjg6NGjb3rd3r17WbNmzfh86FYzQAekeJ6jvOHC\no+z2HS+xfALtG02rFnbxxpiTOCRJ0szp6uoijmO+8Y1vsG3bNnbs2MGLL77I4OAgfX197Ny5k76+\nPgYHB9/0ut/+7d/mpptumrF1GaADUa3VGKvW6DzHGDuApXPzZICD5wjQ1VqN/ROcAd20ujGJwzYO\nSZI0UzKZDHPnzgVgbGyMcrlMJpNh69at9Pf3A9Df38+WLVvGX7NlyxZWr17NVVddNWPr8iTCQJQr\n9VaK803h6MhlWXxJnuETZz/4d/iNMYqV2oQeIGxatagxyu54kfddNm8KK5YkSWkx7/U/I1/c39L3\nHOtaTuEdt13wvkqlwi233MIrr7zCnXfeyXXXXcfIyAi9vb0ALF26lJGREQBOnDjB4OAgzzzzzIy1\nb4AV6GA0A/T5pnBAYxLHOSrQk5nA0bSgK8ei7hyvHnUShyRJmjm5XI7t27eze/duXnrpJX70ox+9\n6eeZTIZMpp5/vvCFL3D33XePV61nihXoQJSqb1+BhvqEje8Pnzzr+mRmQJ9p9cIuZ0FLkjQLTKRS\nPNMWLlzI2rVr2bFjBz09PQwPD9Pb28vw8DBLliwB4KWXXuLrX/86Gzdu5Pjx42SzWbq6uvjoRz/a\n0rVYgQ5EuVIFOG8PNNQr0IfeGGOs+ubJGftHy3RkM/TMndy/T61a1OUkDkmSNGMOHTrEsWPHAHjj\njTd44YUXuPLKK1m/fj1DQ0MADA0NsWHDBgC++tWvsmvXLnbt2sXHPvYx7r///paHZ7ACHYzSRFo4\n5nZQrcHrJ8pvmrix93iJFfM7yWYmNsKuadWCTk6NVXn9xNiE50dLkiRN1PDwMA8++CDVapVqtcpt\nt93GunXreN/73se9997L5s2bWbly5Yz2O5+LAToQpQs8RAhvHmV3ZoDeP1pi5cLJtW8ArG48SPjq\nsaIBWpIktdzVV1/Ntm3bzrq+ePFi4jh+29d+8pOfnKll2cIRitIEWzgADp44/SBhpVrjQKE06f5n\nOD3Kbo990JIkaRYxQAdifArH25wkuPiSPLnMmw9Tef1EmbHq5B8gBJjflePS7hyvHnMShyRJmj0M\n0IGYSAtHLpvhHXM7GC6cDrzjEzgmcYjKmeoPElqBliRJs4cBOhATmQMN9VF2Z1agpzrCrmn1wnqA\nrjqJQ5IkzRIG6EA0e6A73qYHGuqTOIbP6IHed7zEJfksi7pzU/rcVQs7OTVW4/UTZx/QIkmSFCID\ndCDK1YlVoHvndXDsVIXiWD1w7xsts2JBx/gJPpN1+kFC+6AlSdLsYIAORHFsogG6McquUTHeNzq1\nCRxNzQDtiYSSJGmmVCoV1q9fzx133AHAkSNHGBgYYO3atQwMDHD06NHxex977DHWrl3LP/pH/4gd\nO3bMyHoM0IEoV5stHBeuQAMcLJQpV6pnHaoyWfO6clx6Sd4HCSVJ0ox58sknWbNmzfj3g4OD9PX1\nsXPnTvr6+hgcHATgxz/+Mc8++yzPP/88Tz/9NJ/+9KepVCotX48BOhCnTyK8cA801EfZHSiUqdbg\nsilO4GhavbCTV4/awiFJklpv3759PPfcc3zoQx8av7Z161b6+/sB6O/vZ8uWLePXb7/9drq6uli9\nejWXX345L730UsvX5EmEgShXamQzcIECNAu7c3TlMgwXSuw7Xt/+6VSgod7Gse0nR6nWapM+DlyS\nJCXfX+//zxw99bOWvuei7ndy3fIPX/C+Rx55hM9+9rMUCoXxayMjI/T29gKwdOlSRkZGADhw4ADX\nXXfd+H3Lly/nwIEDLV03WIEORqlSoyObueDDgJlMpj7K7kR52iPsmlYt7KJYcRKHJElqre3bt9PT\n08O111573nsymQvnn1azAh2IcqV6wQcIm3rn1mdBL+jKsaArx/yuqY2wa1oyp/7X6NipCr3zpvVW\nkiQpgSZSKZ4Ju3fvZtu2bTz//PMUi0VGR0e5//776enpYXh4mN7eXoaHh1myZAkAy5YtY9++feOv\n379/P8uWLWv5uqxAB6JUqV2w/7mpd14HBwtl9o1O7wHCpnzj+PDmKD1JkqRWePjhh3nxxRfZtWsX\njz/+OGvXruWxxx5j/fr1DA0NATA0NMSGDRsAWL9+Pc8++yzFYpFXX32Vl19+mfe+970tX9cFK9BR\nFP1H4NeBg3EcX9O4thj4CnA58AoQxXF8pPGzh4G7gArwiTiOtzauvw94CrgE+O/AA3Ecm7hapFSp\nXXACR9PSeR2cKFf56eFT/PKq6ZeMm5/bPA1RkiRpJt13333ce++9bN68mZUrV7Jp0yYArrrqKm67\n7TZuuukmcrkcGzduJJeb3n9pP5eJlCyfAm55y7VPAc/FcbwGeK7xPVEUXQ0MAL/QeM3jURQ1V/3H\nwN3Amsb/3vqemoZ6BXqiLRz1qvPJcrWlFegxK9CSJGmG3HjjjXzpS18CYPHixcRxzM6dO/nKV77C\npZdeOn7fAw88wLe+9S2++c1v8o//8T+ekbVcMEDHcfwCcPgtl28Hvtj4+ovAB8+4/kwcx8U4jl8G\nfgJcH0XRcmBBHMffblSdv3TGa9QC5Ur1gsd4NzVnQQNc1oIA3WELhyRJmkWm2gPdG8fx/sbXB4De\nxteXAXvOuO+1xrXLGl+/9bpapFSdeAV66RkBuhUVaFs4JEnSbDLtKRxxHNeiKGppcoqi6B7gnsb7\n09PT08q3D1NmH3O7MxP6XfUA87t+ymixwjWXL2dO59v3BuXz+bd931P5N4CX6Z4z172aIRfaA808\n96D93IP2cw/a72LuwfDwMPl8mAPburq6pvV7nOpvZTiKouVxHO9vtGccbFzfC6w6476VjWt7G1+/\n9fo5xXH8BPBE49taczi2zu9EsURnJsdEf1fvmJOnI5vh5PEjnLzAvT09PW/7voWT9fnPR4+PMjLS\n+kZ9XXgPNPPcg/ZzD9rPPWi/i7kHxWJxRh7AS4JisXjW73HFihUTfv1UA/TXgI8An2/889kzrn85\niqLfB1ZQf1jwO3EcV6IoOh5F0S8Du4A7gMem+Nk6h3KlSme+48I3Nty4ej6FUrUlnz3eA20LhyRJ\nmgUmMsZuM/CrQE8URa8Bj1APznEURXcBPwMigDiOvx9FUQz8ABgD7ovjuNJ4q3/B6TF2f974n1qk\nVKnRmZ34KTz917TuP/+M90BXWxPIJUmSkuyCATqO4w+d50c3n+f+jcDGc1zfDVwzqdVpwiYzB7rV\n8lagJUnSDKpUKnzgAx9g2bJlfOlLX+LIkSN8/OMfZ8+ePaxatYpNmzaxaNEiDh8+zD333MP3vvc9\noihi48azImlLeBJhICZzlHerOQdakiTNpCeffJI1a9aMfz84OEhfXx87d+6kr6+PwcFBALq7u3no\noYf43Oc+N6PrMUAHol6Bbs92ZjMZ8lkr0JIkqfX27dvHc889x4c+dLopYuvWrfT39wPQ39/Pli1b\nAJgzZw7XX389XV1dM7qmMGeTzDK1Wo3yJOZAz4R8NuNBKpIkBWrBa/voeONUS9+zfEk3x1deePLF\nI488wmc/+1kKhcL4tZGREXp768eQLF269KJPh7ECHYBKDao12hqgO7IZWzgkSVJLbd++nZ6eHq69\n9trz3pPJZMhkLm4GsgIdgFKlPv2is00tHAD5XNYWDkmSAjWRSvFM2L17N9u2beP555+nWCwyOjrK\n/fffT09PD8PDw/T29jI8PMySJUsu6rqsQAeg1Aiu7ZrCAfUKtC0ckiSplR5++GFefPFFdu3axeOP\nP87atWt57LHHWL9+PUNDQwAMDQ2xYcOGi7ouK9ABaFZ+294DbQVakiRdBPfddx/33nsvmzdvZuXK\nlWzatGn8ZzfccAOFQoFSqcSWLVvYvHkz7373u1v6+QboAIxXoCdxkEqrdeTsgZYkSTPnxhtv5MYb\nbwRg8eLFxHF8zvt27do142uxhSMA5QT0QPsQoSRJmi0M0AFIQg+0LRySJGm2MEAHIAk90LZwSJKk\n2cIAHYBiQlo4nMIhSZJmAwN0AJJSgbaFQ5IkzQYG6AAkpgfaCrQkSZoFDNABaAZXj/KWJEkhqlQq\nrF+/njvuuAOAI0eOMDAwwNq1axkYGODo0aNvun/v3r2sWbPmTfOhW8kAHYDmUd4dbT3K2xYOSZI0\nM5588knWrFkz/v3g4CB9fX3s3LmTvr4+BgcH33T/b//2b3PTTTfN2HoM0AEY74Fu50EqtnBIkqQZ\nsG/fPp577jk+9KEPjV/bunUr/f39APT397Nly5bxn23ZsoXVq1dz1VVXzdiaPIkwAM0e6M58G3ug\ncxnGrEBLkhSk6jP/ntqel1v6nplVV5AduPuC9z3yyCN89rOfpVAojF8bGRmht7cXgKVLlzIyMgLA\niRMnGBwc5Jlnnpmx9g2wAh2E8RYOK9CSJCkg27dvp6enh2uvvfa892QyGTKZegb6whe+wN13383c\nuXNndF1WoANQqtToyJ7+y9MOzYNUarVaW9chSZJabyKV4pmwe/dutm3bxvPPP0+xWGR0dJT777+f\nnp4ehoeH6e3tZXh4mCVLlgDw0ksv8fWvf52NGzdy/PhxstksXV1dfPSjH23pugzQAShXam2dwAH1\nMXYAY9VaW8fpSZKkcDz88MM8/PDDAHzrW99i06ZNPPbYY/zu7/4uQ0ND/Mt/+S8ZGhpiw4YNAHz1\nq18df+0XvvAF5s6d2/LwDLZwBKFUaX9obbaP2MYhSZJm2n333ccLL7zA2rVr+eY3v8l99913UT/f\nCnQAytVq2yvQzQA/VqlBR1uXIkmSAnTjjTdy4403ArB48WLiOH7b+z/5yU/O2FqsQAegXoFu71bm\nrUBLkqRZwgAdgCT0QHec0QMtSZIUMgN0AIpJCNCNCrinEUqSpNAZoANQrlTb3sLhQ4SSJGm2MEAH\noFSptfUYbzijB9oKtCRJCpwBOgDlJIyxy9kDLUmSZgcDdABKSeiBtoVDkiTNkEqlwvr167njjjsA\nOHLkCAMDA6xdu5aBgQGOHj0KwJ49e7jyyitZt24d69at41//6389I+sxQAcgCT3Q+ZwtHJIkaWY8\n+eSTrFmzZvz7wcFB+vr62LlzJ319fQwODo7/7J3vfCfbt29n+/bt/Nt/+29nZD0G6ACUqjW6ElKB\ntoVDkiS10r59+3juuef40Ic+NH5t69at9Pf3A9Df38+WLVsu6po8iTAApbH290BbgZYkKVxP7h7m\n5SOnWvqeV1zazcd+sfeC9z3yyCN89rOfpVAojF8bGRmht7f+2qVLlzIyMjL+s1dffZV169axYMEC\nHnroIW644YaWrhsM0EGoH+XtGDtJkhSW7du309PTw7XXXsu3vvWtc96TyWTIZOo5ZOnSpXznO99h\n8eLF/M3ZUgL4AAAgAElEQVTf/A2/9Vu/xV/8xV8wf/78lq7LAJ1ylWqNsSrtr0DbwiFJUrAmUime\nCbt372bbtm08//zzFItFRkdHuf/+++np6WF4eJje3l6Gh4dZsmQJAF1dXXR1dQFw7bXXcvnll/PT\nn/6U97znPS1dlz3QKdcMrO2eA91hC4ckSWqxhx9+mBdffJFdu3bx+OOPs3btWh577DHWr1/P0NAQ\nAENDQ2zYsAGAQ4cOUalUAPjZz37Gyy+/zOrVq1u+LivQKVdqBNZ2V6B9iFCSJF0s9913H/feey+b\nN29m5cqVbNq0CYBvf/vbPProo+TzebLZLP/m3/wbLr300pZ/vgE65UqVKgBd+TaPsfMkQkmSNINu\nvPFGbrzxRgAWL15MHMdn3XPrrbdy6623zvhabOFIuWZg7UhIC4cVaEmSFDoDdMo1WzjafRJhNpMh\nl3EKhyRJCp8BOuWS0gPdXEO50VIiSZIUKgN0yjUDa7vnQEO9D9oKtCRJCl37U5empVRNUAU6m7EH\nWpIkBc8AnXLlhPRAQ7OFwwAtSZLCZoBOuZItHJIkKWA33HADN998M+vWreMDH/gAAEeOHGFgYIC1\na9cyMDDA0aNHATh8+DC/+Zu/yZo1a/jMZz4zY2tqf+rStCRlCgdARzZrC4ckSWq5oaEhtm/fzp//\n+Z8DMDg4SF9fHzt37qSvr4/BwUEAuru7eeihh/jc5z43o+sxQKdckqZw5G3hkCRJF8HWrVvp7+8H\noL+/ny1btgAwZ84crr/+erq6umb08z2JMOXGe6DbfJAK1B8itIVDkqTw/O1fn+T40UpL33PBohzX\nXDfngvdlMhn+6T/9p+RyOT784Q/z4Q9/mJGREXp7ewFYunQpIyMjLV3bhRigU67ZA92RhB7oXIYx\nK9CSJKmFvvrVr7J8+XJGRkYYGBjg537u597080wmQyZzcQuJBuiUS9QUjmyGwpgHqUiSFJqJVIpn\nyvLlywHo6enhAx/4AN/97nfp6elheHiY3t5ehoeHWbJkyUVdU/vLlpqWUqVGNgO5JLRw5JwDLUmS\nWufkyZMUCoXxr//yL/+Sq666ivXr1zM0NATUHzDcsGHDRV2XFeiUK1driRhhB40xdrZwSJKkFnn9\n9de56667AKhUKnzwgx/kpptu4j3veQ/33nsvmzdvZuXKlWzatGn8NTfccAOFQoFSqcSWLVvYvHkz\n7373u1u6LgN0yhXHqolo3wBPIpQkSa31zne+k2984xtnXV+8eDFxHJ/zNbt27ZrpZdnCkXblai0R\nI+zAkwglSdLsYIBOuVKllqgKtGPsJElS6AzQKVeuVOnMJmMb87ZwSJIUjFot3P+fPt0/WzKSl6as\nVLGFQ5IktV42m2VsbKzdy2i5sbExstMsPvoQYcqVE9TCkW+0cNRqtYs+0FySJLVWd3c3p06dolgs\nBvP/12u1Gtlslu7u7mm9jwE65UqVGt35ZPyl7mjMoh6rQkeuzYuRJEnTkslkuOSSS9q9jESyhSPl\nSpVqIo7xhvpR3oB90JIkKWjJSF6asqRN4QCcxCFJkoJmgE65pM2BhvpkEEmSpFAZoFMuSRXofNYW\nDkmSFD4DdMqVE9QDbQuHJEmaDZKRvDRlpUqNzmwyKtDNFo4xZ0FLkqSATWuMXRRF/wr4GFAD/hfw\nUWAO8BXgcuAVIIrj+Ejj/oeBu4AK8Ik4jrdO5/Nnu1qtVp8DnZAxdnkr0JIkaRaYcgU6iqLLgE8A\nvxjH8TVADhgAPgU8F8fxGuC5xvdEUXR14+e/ANwCPB5FkdOCp2GsWqMGiTnKu9lKYgVakiSFbLrJ\nKw9cEkVRnnrleR9wO/DFxs+/CHyw8fXtwDNxHBfjOH4Z+Alw/TQ/f1YrNYJqYqZwWIGWJEmzwJQD\ndBzHe4FHgVeB/cCxOI63Ab1xHO9v3HYA6G18fRmw54y3eK1xTVNUbgTopEzhOD3GzgAtSZLCNeUe\n6CiKLqVeVb4COAoMRVH04TPvieO4FkXRpNNUFEX3APc03oOenp6pLjNoY8dPAbB44YIZ/R3l8/kJ\nvf+hagH4GXPmzaenZ8mMrWc2mugeaOa4B+3nHrSfe9B+7kEyTOchwvcDL8dx/DpAFEX/FbgRGI6i\naHkcx/ujKFoOHGzcvxdYdcbrVzaunSWO4yeAJxrf1kZGRqaxzHAdOF4EoPjGCWbyd9TT0zOh9z/R\nWM+ho8cYGbEK3UoT3QPNHPeg/dyD9nMP2s89mDkrVqyY8L3TCdCvAr8cRdEc4A3gZmA3cAL4CPD5\nxj+fbdz/NeDLURT9PrACWAN8ZxqfP+uVk9YDbQuHJEmaBabTA70L+C/AX1MfYZelXjX+PLAuiqK/\np16l/nzj/u8DMfADYAtwXxzHlWmtfpZrPkTYlZAA7Rg7SZI0G0xrDnQcx48Aj7zlcpF6Nfpc928E\nNk7nM3VaqVIFElSB9ihvSZI0CyRjgLCm5PQUjmRsY94WDkmSNAskI3lpSsbnQCflKG9bOCRJ0ixg\ngE6xUsLmQOdt4ZAkSbOAATrFygnrgc5lM2QztnBIkqSwGaBT7PQUjuRsY0c2YwVakiQFLTnJS5PW\n7DVOSgUa6g8SNivjkiRJITJAp1hpLFk90NCsQLd7FZIkSTPHAJ1ipWo9qeYTMoUD6gG6XDVBS5Kk\ncBmgU6xcqdGZy5DJJChA5zI+RChJkoJmgE6xUqWWqP5nqFfDfYhQkiSFzACdYuVKjc4EtW+AFWhJ\nkhQ+A3SKlSpVOvPJ2sJ8NutJhJIkKWjJSl+alFKllphjvJs6sh6kIkmSwmaATrFS4yHCJMnnsvZA\nS5KkoBmgU6xcqdKRoFMIoTnGzgAtSZLClaz0pUlJYgXahwglSVLoDNApVq4mL0A7xk6SJIXOAJ1i\nSZwDbQuHJEkKnQE6xcqVKp1J64HOZRizhUOSJAUsWelLk1JMYA903gq0JEkKnAE6xcqJnANtD7Qk\nSQqbATrFkjyFo1YzREuSpDAZoFMsqXOga4Bt0JIkKVTJSl+asEq1RqVG4irQ+UZLiW0ckiQpVAbo\nlGo+qJe4MXaN9XiYiiRJCpUBOqVKjYDalbAWjmYF2kkckiQpVMlKX5qwUqUKJLkCXW3zSiRJkmaG\nATqlmi0Sye2BbvNCJEmSZogBOqWaLRxWoCVJki4uA3RKNVs4OrPJ2sIOe6AlSVLgkpW+NGHlhFag\nHWMnSZJCZ4BOqVJCe6AdYydJkkJngE6p0w8RJmsLOxotJVagJUlSqJKVvjRh4z3QCatAj8+BtgIt\nSZICZYBOqaRP4bACLUmSQmWATqnmlIukVaCdwiFJkkJngE6p0ycRJmsLfYhQkiSFLlnpSxOW1Ckc\njrGTJEmhM0Cn1Pgc6GyyArQtHJIkKXQG6JQqVWrks5BLWoC2hUOSJAXOAJ1SpUp1fOZykuStQEuS\npMAlL4FpQsqVWuL6n6FeEc9mYMwKtCRJCpQBOqVKlVriZkA35bMZK9CSJClYBuiUSmoFGup90AZo\nSZIUKgN0SpWq1cTNgG7KZzO2cEiSpGAlM4HpghJdgbaFQ5IkBcwAnVKlJAfonBVoSZIULgN0SpUq\nyW7hKFer7V6GJEnSjEhmAtMFJboCnc14lLckSQqWATqlypVa4o7xburIZTyJUJIkBcsAnVJJr0D7\nEKEkSQqVATqlypUqnQnugbaFQ5IkhSqZCUwXVKomuAJtC4ckSQqYATqlyok+yjtrC4ckSQqWATqF\narVa4nugbeGQJEmhMkCnULO6m9g50LZwSJKkgCUzgeltlRrhNMkVaFs4JElSqAzQKdSs7iZ5DrRH\neUuSpFAZoFOoVKkfk53UCnTeCrQkSQqYATqFyuMtHMncPh8ilCRJIUtmAtPbSnwPdC5DtQYVQ7Qk\nSQqQATqFmgE6uXOg6+uyCi1JkkJkgE6h0z3Qydy+ZrB3lJ0kSQpRMhOY3lY54RXo5nQQHySUJEkh\nMkCnUKma/B5osAItSZLCZIBOoaRXoO2BliRJIctP58VRFC0CngSuAWrAbwF/B3wFuBx4BYjiOD7S\nuP9h4C6gAnwijuOt0/n82arZA92V1B5oWzgkSVLAppvA/gjYEsfx/wG8B/gh8CnguTiO1wDPNb4n\niqKrgQHgF4BbgMejKMpN8/NnpcRP4bCFQ5IkBWzKATqKooXArwD/ASCO41Icx0eB24EvNm77IvDB\nxte3A8/EcVyM4/hl4CfA9VP9/NmsnPQ50LZwSJKkgE2nheMK4HXgT6Ioeg/wIvAA0BvH8f7GPQeA\n3sbXlwHfPuP1rzWunSWKonuAewDiOKanp2caywxPvusNAJYvfQcdF6GNI5/PT2oPlryRB15jzvz5\n9PQsmrmFzSKT3QO1nnvQfu5B+7kH7eceJMN0AnQeuA64P47jXVEU/RGNdo2mOI5rURRNugwZx/ET\nwBONb2sjIyPTWGZ4jo4WyABHDx8ik5n5KnRPTw+T2YOThZMAjBw+ykj32Ewta1aZ7B6o9dyD9nMP\n2s89aD/3YOasWLFiwvdOp3z5GvBaHMe7Gt//F+qBejiKouUAjX8ebPx8L7DqjNevbFzTJJUrNTpy\nmYsSnqeiI1v/a+VDhJIkKURTDtBxHB8A9kRRdFXj0s3AD4CvAR9pXPsI8Gzj668BA1EUdUVRdAWw\nBvjOVD9/NitVa4ntfwbIN/5W2QMtSZJCNN0G2vuBp6Mo+hvgHwK/B3weWBdF0d8D7298TxzH3wdi\n6iF7C3BfHMeVaX7+rFSuVBN7jDcw3pftFA5JkhSiac2BjuP4u8AvnuNHN5/n/o3Axul8pqA0luwK\ntFM4JElSyJJbxtR5laq1xM6ABudAS5KksBmgU6jewpHcAG0FWpIkhcwAnUKlSm180kUSdViBliRJ\nAUtuCtN5lSvp6IF2jJ0kSQqRATqFSgkP0NkMZLCFQ5IkhckAnUL1g1SSu3WZTIaOXMYWDkmSFKTk\npjCdVzHhDxFCvY3DFg5JkhQiA3QKNY/yTrJ8NmMLhyRJCpIBOoWSfpQ31GdB28IhSZJCZIBOoaQf\n5Q22cEiSpHAlO4XpnOpzoJNdgfYhQkmSFCoDdMpUqjWqNejMJztA2wMtSZJCZYBOmVKjqpv0Hmhb\nOCRJUqgM0ClTqlQBEn2UN9RbOMYaa5UkSQpJslOYzpKWCnQ+m6FsfpYkSQEyQKdM88G8pM+B7shm\nGKuaoCVJUngM0CnTbOFIegXaKRySJClUBuiUaT6Yl/w50FkfIpQkSUFKdgrTWVLTA52DMSvQkiQp\nQAbolElPD7QVaEmSFCYDdMoUx3ugk711+ZxzoCVJUpiSncJ0lvRUoDO2cEiSpCAZoFNmvAc6m+wA\nnfckQkmSFCgDdMqkpgKdy1CtQcUQLUmSAmOATplSSnqgOxoV8jEDtCRJCkyyU5jOUk7LGLtGgLaN\nQ5IkhcYAnTKlFLVwgLOgJUlSeAzQKVOqVMlnM2QzCQ/QVqAlSVKgDNApU6rWEt++AadbOOyBliRJ\noTFAp0y5Ukt8+wacbuEo28IhSZICY4BOmVKllvgZ0GALhyRJCpcBOmXKlSodCR9hB1agJUlSuJKf\nxPQmpUqNrnzyK9D2QEuSpFAZoFOmVKmNt0ckmS0ckiQpVAbolClXqumYwjHewlFt80okSZJaywCd\nMqVKLR090LZwSJKkQCU/ielNymmZA+1DhJIkKVAG6JQppWUOtD3QkiQpUAbolElLD3SzzcQKtCRJ\nCo0BOmVKlRqdKeiBdoydJEkKVfKTmN7EFg5JkqT2MkCnTGqO8m6E/DFbOCRJUmAM0ClSrdUYq6aj\nhaNZJLcCLUmSQpP8JKZxzQfy0tDCkclk6Mhm7IGWJEnBMUCnSDNAp2EKB9SDvlM4JElSaAzQKVKq\nNgN0OratI5uxhUOSJAUnHUlMQH0GNKSjhQPqo+xs4ZAkSaExQKdI0RYOSZKktjNAp0iaHiKEegXa\nFg5JkhQaA3SKlBotHKnpgc7ZwiFJksKTjiQm4IwpHCk4SAUaFWhbOCRJUmAM0ClSSlkLh1M4JElS\niAzQKeIcaEmSpPYzQKdI2nqgHWMnSZJClI4kJiCFLRy5DGNWoCVJUmAM0ClSSlsLRzZDuVpt9zIk\nSZJaygCdIs0wmpYKtC0ckiQpRAboFDldgU7HtvkQoSRJClE6kpiA+hSObAZSUoB2jJ0kSQqSATpF\nSpUanbkMmUw6EnRHLmsLhyRJCo4BOkXKlSodKWnfAE8ilCRJYUpPGhPFSi01x3hDvYWjUoNqzRAt\nSZLCYYBOkXKllpoJHAD5xlqtQkuSpJAYoFOk2QOdFh2Narl90JIkKSQG6BRJYw804CQOSZIUlPx0\n3yCKohywG9gbx/GvR1G0GPgKcDnwChDFcXykce/DwF1ABfhEHMdbp/v5s0mpmrIKtC0ckiQpQK0o\nZz4A/PCM7z8FPBfH8Rrgucb3RFF0NTAA/AJwC/B4I3xrgsq2cEiSJLXdtAJ0FEUrgVuBJ8+4fDvw\nxcbXXwQ+eMb1Z+I4LsZx/DLwE+D66Xz+bFOqVFMVoG3hkCRJIZpuBfoPgYeA6hnXeuM43t/4+gDQ\n2/j6MmDPGfe91rimCSpVaqnqgW62cIzZwiFJkgIy5R7oKIp+HTgYx/GLURT96rnuieO4FkXRpNNT\nFEX3APc03oOenp6pLjMoFV5h/pzui/77yOfzU/rMJYUssJc58xfQ07Og9QubRaa6B2od96D93IP2\ncw/azz1Ihuk8RLgW+CdRFP0a0A0siKLoPwPDURQtj+N4fxRFy4GDjfv3AqvOeP3KxrWzxHH8BPBE\n49vayMjINJYZjlOlMarlEhf799HT0zOlzzxZOAHAocNHGekotXpZs8pU90Ct4x60n3vQfu5B+7kH\nM2fFihUTvnfK/QBxHD8cx/HKOI4vp/5w4PNxHH8Y+BrwkcZtHwGebXz9NWAgiqKuKIquANYA35nq\n589G5ZT1QHfYAy1JkgI0Ew21nwfWRVH098D7G98Tx/H3gRj4AbAFuC+O48oMfH6w0toD7Rg7SZIU\nkmnPgQaI43gHsKPx9SHg5vPctxHY2IrPnG1qtRrltM2BdoydJEkKUHrKmbPcWBWqNVIVoB1jJ0mS\nQmSATolytT4psDOVLRzVC9wpSZKUHulJY7NcqdFH3GEFWpIkqa0M0ClRGquH0DS1cNgDLUmSQmSA\nTokT5frAkrmduTavZOLyTuGQJEkBMkCnxGixHqDndaZnyzqy9bXawiFJkkKSnjQ2yxVK9QA9P00V\n6MbfLls4JElSSAzQKVEo1SdZzOtKT4DOZDLksxlbOCRJUlAM0ClRKKavAg31Bwlt4ZAkSSExQKfE\naKlCRzaTqikcUB+7N2YFWpIkBcQAnRKFUoV5nVkymXQF6LwVaEmSFBgDdEqMFqup6n9usgItSZJC\nY4BOiUKpkrr+Z7AHWpIkhccAnRKFUiW9FWgDtCRJCogBOiUKxQrzUliBdoydJEkKjQE6JUZL1VSd\nQthkC4ckSQpN+hLZLFSu1Dg1Vk1lD3Q+ZwVakiSFxQCdAicax3insgc6aw+0JEkKiwE6BQrNAJ3G\nCrQtHJIkKTAG6BQYbQTo+WmsQNvCIUmSAmOAToFCsQqQ2ocIbeGQJEkhSV8im4VGbeGQJElKDAN0\nCjR7oNM4haN+lHe13cuQJElqGQN0CowWK2SAOSlt4bACLUmSQpK+RDYLnShVmNeZJZvJtHspk5a3\nB1qSJAXGAJ0Co6VqKmdAQ6OFowrVmiFakiSFwQCdAoViJZUPEAJ0ZOt/xaxCS5KkUBigU2C0lOIA\nnau3nRigJUlSKAzQKVAoVVI5gQPqPdCAh6lIkqRgGKBToFCqMq8rnVvVrEA7iUOSJIUinalsFqnW\nao0pHFagJUmSksAAnXAny1WqtXSeQginA7Q90JIkKRQG6IQrFBunEKZ4jB1YgZYkSeEwQCfcaOMY\n73kpPIUQ6icRgj3QkiQpHOlMZbNIoVQFSO0UDsfYSZKk0BigE67ZwpHWkwh9iFCSJIXGAJ1whUYL\nR2or0D5EKEmSAmOATrhmD/TctAZoHyKUJEmBMUAnXKFYoTufGQ+iaZP3IUJJkhQYA3TCjZaqqZ0B\nDbZwSJKk8BigE+5EqZLaGdBgC4ckSQqPATrhRovpPcYbrEBLkqTwGKATrlCqpPYQFYB8swJdrbZ5\nJZIkSa2R3mQ2S4TSA20LhyRJCoUBOuEKxXT3QDuFQ5IkhcYAnWDFsSrlai3VFehMJkM+C2NWoCVJ\nUiAM0Ak2fgphiivQAPls1gq0JEkKhgE6wUaLzVMI071NHbmMPdCSJCkY6U5mgSuU6pMr5qe4hQPq\nDxI6xk6SJIXCAJ1go40WjjT3QEP9QUJbOCRJUigM0AlWKIbRA20LhyRJCokBOsEKgVSgbeGQJEkh\nMUAnWKFUJZeB7nym3UuZlrwBWpIkBcQAnWCjxQrzunJkMukO0LZwSJKkkBigE6xQqqR+AgfUWzh8\niFCSJIXCAJ1go6VK6vufoV6BtoVDkiSFwgCdYCdKFeZ3pX+L8llbOCRJUjjSn84CNlqsBlGBdg60\nJEkKiQE6wQoBtXBYgZYkSaEwQCfUWLXGyXKVeSk/RAUcYydJksJigE6oE41DVJzCIUmSlCwG6IQq\nlKoAzOtM/xbZwiFJkkKS/nQWqOYx3vMDaOHwKG9JkhQSA3RCjRbrAXpuAC0c+cYc6FrNEC1JktLP\nAJ1QhcB6oAGr0JIkKQj5qb4wiqJVwJeAXqAGPBHH8R9FUbQY+ApwOfAKEMVxfKTxmoeBu4AK8Ik4\njrdOa/UBa1agQ5jC0ZGrB+hytUZH+v84kiRplptOBXoM+GQcx1cDvwzcF0XR1cCngOfiOF4DPNf4\nnsbPBoBfAG4BHo+iyDh1Hs0K9NyO9P9HgnyzAu2DhJIkKQBTTmdxHO+P4/ivG1+PAj8ELgNuB77Y\nuO2LwAcbX98OPBPHcTGO45eBnwDXT/XzQ1coVZnbmSXXCJ9p1pGt/zVzlJ0kSQpBS8qbURRdDrwX\n2AX0xnG8v/GjA9RbPKAervec8bLXGtd0DoViGKcQwhktHFagJUlSAKbcA90URdE84E+BB+M4Ph5F\n0fjP4jiuRVE06dQURdE9wD2N96Cnp2e6y0ydIsMsmtOViD97Pp+f1joWH6oB+5m/cBE9i+e0bmGz\nyHT3QNPnHrSfe9B+7kH7uQfJMK0AHUVRB/Xw/HQcx/+1cXk4iqLlcRzvj6JoOXCwcX0vsOqMl69s\nXDtLHMdPAE80vq2NjIxMZ5mpdLjwBpfksyThz97T0zOtdbxxogDAwUOHmVs92aplzSrT3QNNn3vQ\nfu5B+7kH7ecezJwVK1ZM+N7pTOHIAP8B+GEcx79/xo++BnwE+Hzjn8+ecf3LURT9PrACWAN8Z6qf\nH7rRYpV3zO1o9zJawhYOSZIUkulUoNcC/w/wv6Io+m7j2qepB+c4iqK7gJ8BEUAcx9+PoigGfkB9\ngsd9cRxXpvH5QTtRqgQxAxrOmMLhQ4SSJCkAUw7QcRz/FXC+ERE3n+c1G4GNU/3M2aJWqzFaqgRx\nCiGcPkjFKRySJCkE6R8yHKA3xqpUazC/K4ztydvCIUmSAhJGQgvM+CmEVqAlSZISxwCdQIVSFSCY\nHujmQ4SeRChJkkJggE6g8Qp0VxgBOm8FWpIkBcQAnUAnSvUAHVwF2gAtSZICYIBOoNFGgJ7bGcb2\njPdA28IhSZICEEZCC0yhWO+BDuUhQudAS5KkkBigE2i0VKEzl6ErH8b2eBKhJEkKSRgJLTCFgE4h\nhDMfIqy2eSWSJEnTZ4BOoEKpEswEDoBsJkM+C2PmZ0mSFAADdAIVihXmBfIAYVM+m6FcMUFLkqT0\nCyulBWK0VA3mAcKmjmzGOdCSJCkIBugEKhQrzA+ohQMgn8s6hUOSJAXBAJ1Ao6VKgBVop3BIkqQw\nGKATplSpUqrUgprCAZDPZm3hkCRJQTBAJ0yhVH/QLpRTCJs6chkr0JIkKQhhpbQAFIr1Y7xD64Hu\nyGbsgZYkSUEwQCfMaKkeoEPrgc47hUOSJAXCAJ0wwVagcxnGbOGQJEkBMEAnzOkKdFhb4xxoSZIU\nirBSWgBONB4iDK0Cnc/ZAy1JksJggE6Y0WKFbAYuyYe1NR1Zp3BIkqQwhJXSAlBoHKKSyWTavZSW\nsoVDkiSFwgCdMCGeQgiNFg4r0JIkKQAG6IQpFCvM7wpvW6xAS5KkUISX1FKuUKoGW4G2B1qSJIXA\nAJ0whUBbODyJUJIkhcIAnTCjpQrzAhthB6dPIqzVDNGSJCndDNAJUqnWOFGqMj+wQ1SgfhIhwFi1\nzQuRJEmapvCSWoqdKNfTZagtHADlqglakiSlmwE6QQrF+jHeoZ1CCFagJUlSOAzQCVIo1QN0iBXo\nfLMCXTFBS5KkdDNAJ0jIAbrZwuEkDkmSlHYG6AQZbbRwzAvxIJVc/c/kYSqSJCntwktqKVYo1dsb\n5gdYgc43/qZ5nLckSUo7A3SCjAbdwmEFWpIkhcEAnSCFUoVL8llyjX7hkDSncHictyRJSjsDdIIU\nihXmB9j/DD5EKEmSwhFmWkupQqkSZPsGQN4KtCRJCoQBOkFGi1XmBXiICpx5EqEBWpIkpZsBOkEK\npUqQEzjgdAXaFg5JkpR2BugECbmFY7wCbQuHJElKOQN0QtRqtUaADnNL8rZwSJKkQISZ1lLo1FiN\nsSrh9kD7EKEkSQqEATohCo1DVELtgXaMnSRJCkW+3QtQ3WixcQphoBXopLdw/LcfHuInh05xxaXd\nvGtxN++6tIuF3f6fhyRJOpsJISGCr0A3p3AksIXjteNFvvjS63Tns3zzZ6Pj15dckuddi7u44tJu\nrlzczbsu7eYdc/NkMuGdFClJkibOAJ0QzQAd6kOE2UyGXCaZFegvf2+EzlyGP/4n7yKXyfDykVP8\n9Mgpfnq4yE+PnOLFfSdoLntBV45/deNyrlsxr72LliRJbWOATohCqQqE28IB9TaOpPVA//TwKXa+\nOttU+iMAACAASURBVEp0zRIWNVo2rl02l2uXzR2/pzhW5ZWjRX56+BRf+9ER/vg7B/h3v/4uuvJh\n/suOJEl6eyaAhGj2QIfawgH1No5ypdruZbzJ0997nbmdWW7/+cXnvacrn+Wqnkv4wLsv5ePX93Lw\nxBj/7YeHL+IqJUlSkhigE6JQqtCRzdCZC7e/tiObSVQLxw9fP8nufSf4v69eMuEDbK5dNpf/c9V8\n/sv3D/H6ifIMr1CSJCWRATohRosV5nXlgn5ArSOXnBaOWq3Gf/7u6yzqzvHrV106qdd+9Lp3UKvB\nl156fYZWN3mVao2/3legkpDfryRJITNAJ0ShVA32AcKmfDaTmINUvnfgJH978A36r1lC9yR7mXvn\ndfIbVy/mhZ8d5wcHT87QCifnT39wiN/5i9d41tYSSZJmXNiJLUUKpUrQ/c//f3t3Hl/HXd///jVz\nFuloXy3Lknc7XpKQ3XZIAjEkIZQQUkK+CVDWsrS9zeXSAo/wKNBwuZQApUB7S1tuQwltWb6/HyUJ\nJGnIQpytxE4cb3EcO/GuXdZytJ5l5nv/mCNZkuVFtqTv2Ofz9GOsoznSOR+d98yc7/nOzHcAYq4b\nikM4jDH8x9YOaouivGNZxWk9xq3nV1OdiPKvL7VZ7/XtGMjwv3YcIerCL3Z0cmRQDi0RQgghZlLe\nNKA3bt3CY0+/wED6iO1SJtWf9s7pETgAohEnFONAv3C4nz1HhrnjTTXEIqe3ChRGXT5ySS1vdKV4\ncm/vNFc4NT/a3A7AV9+2AM+HH28Oz6ElQgghxLkoLxrQB1sO0bprPsMtK/jtC7/m9a4nMCZco0H0\npbxTPpHtbBUNwUmEnm/46dZO5pXGWb+4/Iwe6y2LylhVm+Dft3YwkBvHe7ZtaRng+YN93HZ+NRfU\nFfHe84NDS3a0hePQEiGEEOJcdM43oNOZNFs3DkN0gIraDNGWm9i877/53f5v0Jdqs13eqOAQjnM7\njmAYO7sN6GcOJDnQm+IDb6oh4p7ZCZuO4/CJy+pIDnvoHbO/ZyPjGf6/F9uYWxLjltXBMHy3rq5m\nTnGUH75o/9ASIc5m0VQzFYf/hbLWn8E0dLgYYzjUm6K1L81A2sMYWT+FOJud8xdS+f2LO2B4Ccsv\nb2NJQyNP/XcfhW1/QU/h13j0jS9ywZxbOa/6nbiOvcZrxjMMZ8053wMdcx36s/Z6/rO+4WfbOllc\nWcBVC0un5TGXVRfy9qXl/HpXF9cvK6exrGBaHvdUPLS7i8PJNF96ayPx3KEoBVGXj19Wxz1PN/HI\nnm5uWnH88a2FEMdyvGGKux4j0fs/GCeOO7yfbLyewaprT/sxjQk+7D60u2d0XsQJrmxaWhAZ8zVK\naUGEhrI4axpLzvn3BCHOZud0A/pAywF6Di4kMWcfq5ZeAsDFa4rY+IxhWerr9Nb8mK1tP+dQciNX\nzPsEFYXzrdQ5svv/XD8G2vYwdk+80Utrf4YvX9uIO43DBX7oolqeP9jHj15q5yvrZ2cZ6hrK8rNt\nR7h8XjFXNI6/rPi6xhIuri/mp1s7uXph2egVFs8F+7qHuf/VLt48v5S186fnQ5AQABhDQf92Sjp/\ng+v1M1S+loGqGyjteIDirt+SKZxPpmjpaT30g7u6eWh3DzcsK2dVbRHJVJa+lE8ylSWZ8uhLeRxO\npulLDZFMefgmOOTtsnnFXL2wjDWNJVMeLUgIMbPOnXfWCdKZNNs2piE2wJvXLR+dXzcvxsKlcQ7s\nSbNu3p+zsPElNrf8hMf2fplVNTezquZmIu7svix9Iw3oc7y3weYwdmnP5xfbO1lRk+CyecUn/4Up\nqEhEuf3Cav5tcwcvNvVzeUPJyX/pDN33cjtZ3/CJy+uOuc9xHD55+Rw+89A+/n1LB3euq5/xemZa\nW3+a/9zaydP7kwA8tS/J7RdWc8eFNdP6YUjkh4xn6BzMUF8aByCS7qS04wHiQ6+TKWigt/5DZHMd\nKn1z/pBoqoXytp/TNf9O/GjZlJ7r+YNJ/m1zO1fOL+VP18w96fLqG8PrR4Z55kCSZw/08cLhfgoi\nDmsaS7hmYRmXzis+7ZOfhRDT55xtQP/PmEM3ihPjGzSrL07Q2Z5ly8ZB3nrjGm5ctpqXW/+DVzp+\nxeHkJtY2/AmViYWzVmv/yGW8T9ADnfGGOND7HA2ll5GITe3CH2ERc+31QD+yu4cjQ1k+e1X9jFys\n5l3nVfHonl7ufamdi+YWE5vBK0rubB/kqX1Jbju/erQBMFFjWQE3r6ziv3Z2ccOyClbUJGasnpnU\nM5xF7zjCo3u6cR2H966u4t0rq/jJlg5+sf0I+7pTfPbN9RTFzu0Pn2L69Kc9vv7UYXZ2DLF+YYL/\na9kuqgaexTgR+mrezVD5OhhzSJ9xC+it/yCVh/6Rstaf0dPwCXBObXl7rXOI7z7fwnk1hXz2zfWn\n9GHPdRzOq0lwXk2Cj106h53tQzxzIMlzB/t45kAfxTGXdfNLuWZRGW+qKzrjczmEEKcncvfdd9uu\n4WTu7uvrm9Iv7G8+wMHtdSTm7GftpReMzjfG4DgOrutQWR1h3+40g/0+8xeUML98DZWJRRxObuL1\nrscpK2igrGDedP8tk9rXPcwzB/q4aUUl1UWxcff5xmdvzwaePfg9DiU3crD399QWryIRO73xi09H\nUVERg4NnPqrDi8397D4yTFHMpTgeoTTuzsqVFwczHt96ppmVtQnuuLB2Rp4j4jrMLYnx0O5uSuIR\nVtZOb4N1JAPPN3zj6SairsPnr2kgeoI3z/NqCnlyb5Kd7UNct7T8rOqpHcx4/PKVLr79bDOvdQ7x\n9qXl3PWWBq5cUEYi5rK2sYSygii/ea2b3x/q56L6IsoKZrY/YLrWA3H6zjSD7qEsf/3kIfZ1D/PJ\nFT3cXvIg9f5uXssuJzX/o5iS5TDJemIixXixSop7nwOTIVO0fJJHH6+lL82XnzhEWUGEr719AcWn\nsYfRcRzmlMS4oqGEm1dWsao2gWcMvz/Uz2Nv9PLE3l7Snk9DWXzWDvGQ9cA+yWDmlJaWAnz1VH72\nnGtApzMpnt/QBY7HW9c3Eo/Fae/PcM8zTfzvV7pYWlVITXGMwoSL48C+PWmKSiKUV0QoK6hnQfmV\ntA/uZPeRR4m4MWoSy2e8kfda5zAvHO7n1tXV446Dbh/YxXOHvs/e7t9RmVjIRXW30zawk9e7HpvV\nBv50rqw7O4Z4en+Sh3Z388QbvRzoTZHKGioKIzP2BvBfO7t4sXmAz10175gPKNNpXlmcPUeGeGpf\nkuuWlk/r3zOSwX/v6eHxN3r587VzWVJVeMLfiUVcKgsjPLy7h5riGEtP8vNhkPEMj+zp5ptPN/Ni\n8wBXNBRz1zUNXLe0Ylwvs5PrpTt/ToIn9yZ5dE8PiyoKmFc2eY/8dDib37SGMj4t/WnKCiKz8qF1\nppxJBq19ab70+EGGhvu5b+1W3hJ/moKCUn505Abu3rGMJ/cPU14YYWFFwaSvkVcwF8cboLj3eTLx\nerz4nOM+VzLl8eXHD5HK+vw/1y2ktvjMtzuu41BfGmfd/FJuXlnJksoC2gYyPPZGL795rZvmvjTV\nieiMbuPg7F4PzhWSwczJ6wb0sy9sI9XVyHmXJamvncvv9iX5+lOHaDi4g2Vde7m3KcqwcVldW0RN\nbZTO9iyH9qVpWBgjFneJRQpZWH4V/elW9nQ9ymDmCHNLLprRUTq2tw3ycssA77+whnjUZSDdwabm\ne9nW9nNcJ8rl8z7GxXUfoKJwPgvK1h1t4DsxaopmvoE/XStrY1kB715ZxbWLy2gsi5Px4aXmfjbs\nT/KrV7t44XAfrX0ZDFBTFJ2WXZN9KY9vP9vMpfOKuWV19Rk/3sksrSrkN691k0x5rGkombZsioqK\naOlK8o2nm1hVk+DDl9Se0mMvrChgW+sgzxzo4/qlFRSE9EQkzzc8vT/Jt55p4un9fSyvLuRzVzdw\ny6pqyk5wEmRdSZyrF5SxpWWAB3d147qwujYxI+vE2famZYxhV+cQP9/eyd//voVf7+pmV+cwS6sK\nKT9LTyw93Qz2dw/zpccPsqrwMP90wZNU+k0MVq6nr/79rGxcwMVzi9jZMcTDu3t4uWWAhRUFkzZE\n00XLiA/uIdG3iVTxBZhI0bE/4/l87XeHOdSb5ivr58/IB9eI6zC/vIC3LSnn6oWl+MbwzIE+HtnT\nw0vN/URdh4ay+Iwc3hHG9WAw4/Ho6z1sbxukKZmmYyBDMuWRyvoYgsMHz6Y9cCcTxgzCyjeG1zqH\nefpAcP5MTVH0hO8PU2lAO2fBWJSmubn5lH5wf9N+tj1bRqLuAGvWXcgPNrbSu30bf7L/QYY9l2Ss\nhGX9h/lt/RVsP/86PrX+PGpjUTY82kdZeYQ3ry/ByW1wjPF5peNXvNJxP7VFK7hq/mcoiM7MWf//\nubWD/7XjCPqOhbzW+Rt2HXkEB4dVNTexouYPiLrjh0bL+mk2Nv2QQ8kXWFRxDZfXf3xGT3ysqamh\ns7NzRh7b8w17u4fZ0jLAltZBdnUMkvWhsjDCTSuruHF5xWmdXJnK+jyxt5cHXu2irT/D99+1mIUV\nszPE3I9eauOBXd3UFkW5qL6Yi+cW86a5RWfUaKmpqeGrD23niTd6+d67FrOg/NT/lv3dw3z2kf3c\nuLyCT18x97RrmAmeb3jmQJJfbD9Cc1+aRRUFfPjiWi6dVzylRnAq6/OPL7SyYX+SK+eX8pkr60nE\npvfDwkyuB2MZY3i1Y4hH9/SQ9g0raxKsqk2wpKrwhIfsjOgayvLU3l4e39tLUzJNYdThqgVlzC2N\ncf/OLoayPu88r5L3X1hzwvMuwuh0MtjZPsi3Nuzn042beU/tK2RjNSTr1OhJgiN8Y/jd3l7+fUsH\n3cMe1y4u48MX1x7TkHYz3VQd+ge8aAXdjX8KbmzcY3znuWaePdDH566axzWLpnbC4ZkYzHj8bm+S\nh3d3czgZ7G24YVkFNy6vmJYe8BGztR6cioxnePT1bvT2I/Smjn8xKwcoibuUFkQoL4xSXxqjobSA\nhvI4DWVx6ktiZ9WJmWHKIIxSWZ8trQNsPNzPpqZ+eoePLhsNZXGuW1LO+iXlVCaOfU+eN28eBIvM\nSZ0VDejnn95Hw8I4sdjx/6Z0JsWjDx3C+DGqL0nwm6de5dZdD9ATK+fh+VfzRkkDAEV+mrXt21jX\nsZ3OgnLiN/4hK+cuYevGIVZeWMjy1eN7C/b3PMem5nspilVyzYK/nJHDJv5lUzP7up/jyoVPM5zt\nYUH5lVxUdztFseP3mBpjcg38X814A382V9bhrM/21kF+s7ubLS0DFEZdblxewbtXVlJzCrsmk8NZ\nHt7dw0O7g17g86oLuePCGi6bhZExRmQ8w+Nv9LCldYDtrYMMZIKxr5dUFnBxfTEXzS1mVW1iSr3B\nHdkCPvmLrdy8spKPX3bsyBsn88MX23hkdzffuXHRSQ/9mA0jPc56RyfNfRkWVRRw+4XVrJtfeto9\nRcYYHtzVzY9fbmd+WQFffGvDcU+yPB0zvR6ksj4b9gcNoH3dKYrjLsWxCO0DGQDiEYfl1YWsqi1i\nVW2CFTWJ0QZw1je82NTP42/08lJzP76BVbUJrltazpsXlI4e/pIczvLTbZ08+noPxTGX97+plhuX\nV5w1J6JNNYMXm/q5/6WtfGXpszQW9DBYfiX91TeCe/zlYuT4+wde7cJ14IZlFVxSX8z5dUWjh2XF\nB16jouXHDJVeTl/draO/+5OX2/nlzi4+cnEt7z1/5vd4TcYYw7a2QR56rZtNTf0YAytrE6xpLGFN\nY8kZj1UfhsabbwzP7E/y022dtPZnuKCuiI9cXMuCigL6Ut7o0IBHv2ZHv+8e9mhOpukayo4+nuvA\nnOIYDWXx0WluSXzcON1h2nsXhgzCpmc4y4tN/Ww83M/LLQOkPUNRzOXSecWsaSjh/LoitrQM8MQb\nvezsGMJ14LJ5JVy/tJzLGkpGOydC3YBWSt0IfB+IAP+qtb7nJL9i/uW7O3FNhvrCIyxYXEjV6gbc\n2PjG1FPPb6bv0BL6y7Yy54UNHIrX8sS8NSRjxSyMZHh37w7qho7wXONank0X029cytL9rOvYToPp\np/ai99GdTHD1dSVUVI3/VNI5uIdnD34P32R58/w7mVtyAWcq4w1xqGsLB7s20zSwk2hBktLofC5v\n+AhzSlac8uMc6Hmejc3/OqMN/OlYWY0x0NcbTKeiopp9qSj3v9rFMweSOASXzr5lVRWLKo9tALb1\np3ng1S4ee6OXtGe4oqGYP1xdPW2784eHh2lpaaG9vZ3S0lLmzZtHeXn56GMb4zOU7SaZaqYv1cpQ\ntodEtILCaCWdgyXs7ixka2twvHvWD3Yprp6TYHVt0biN9mQbad8Y/uqJJpp7h/inm5eMNoaMMfSn\n22gb2EFr/yv0DO+nrKCBmqLl1BSdR1Viyejei/60x589uJd5ZXG+cf2CKb8mnm9o68/QlExzOJmi\nKZmmKZmmfSDDvLI4q2oTrKotYkVN4QlHxPB8w4Zcw7kl13C+48Ia1s4vmbZdrFtaBvjbZ5vI+LCy\nppAlVYUsrixkSVUB80rjp/08M/Wm1dKX5pHd3Ty+t5eBtM+iigLetaKStywqozDqcmQww66OIV7N\nTXu7hxkZzGZ+eZxFFQVsaxukd9ijsjDC+iXlvH3piS/qs797mHtfamdb2yDzy+N84rI6Lq4vxs1m\nwffxY7FJT6azbSoZbNjbRe/Bx/howzZMtIT+uved0sl/I9r609z3cgcbD/eT8Q1R12FlbYKL5hZx\n8dxiLuIZSnt+R3LOrQyXXc6je3r4wcZWblxewZ9cUTdm22BIJpO0tLTQ3d1NUVERJSUllJaWUlJS\nQiIxM4ccAbT3Z3hyby8vHO5jb3cKCHrg1jSUsLaxhPNqElP+8GSz8WaM4eWWAX6ypYN93SkWVwZ7\nrC6pn9oeKwg+KDUnM+O2Z819wdf0JMOtFkSc0cb02Ib1qfZcxyMOBRGXgqhDQdQNvo+6FOTmx6MO\nEcch7RlSnk86G3xNZX1SniGV9YP7sj7xwgRDQ0Mnfc6Y64w+38hzH60j+D7qOqOPO/I1Nfp1pA4z\nrSNoOQTXhRj9+ye+Hrl6XYfR1yOV9UmNviaGtBfUOZzxebVjiNc6h0YP/1zbWMKaxlLOn1M06YhY\nTck0T7zRw5P7knQPZSkvjLB+cbDdXLdq8UiJJ/87ZrMBrZSKALuB64HDwCbg/VrrnSf4NfNPd3+X\nIuaSLj4PP5qgaKidxtQuGmtTFC1fzP5ohO3bFuIOvcbW3mZerF4FOKxze7ipdzsri9M4y1YFD7Z1\nE7Q0sXXFdTybWMBzgzGGnSh1qSTvLFxAosDl2rcnKJhwWe3BTCf/c/gfSaZauajuDpZUXQsFhTju\nyVceYwzdPV0caN9Ka992+sxe/MJuHNdgPId0TynDrdUMt1YTdRzqihPUV1dT3zif+qXLKKw48bB1\nM9HAH2sqG0yTyUB7C7QdxrQ2QWsTpq0JWg/D4MDUnrh6DsxfQkfjCn5dsIzHegpIeXDZvGJuWVXF\nhXVFvNGV4levHuH5g324Drx1UTm3rK6a0iEOx/wNxtDT00NLSwvNzc2jb3xgcCI+kaJhokXDFJZn\nKao0RIqGybo9+GTGPIoDjF+3Ik6cwmgVviknOVxKS18xLX3FDGTKGUyXMZgpo7JwbIO6gIayOAd6\nUvxoczufubKeqxa4tA28Qlv/K7QO7GAwE+RSFKuhsmARfelmkunmXAURKhMLqUkEDeqd7XX8YOMQ\n/8fauVw6rzjYGI1sJEc2mGM2UJ2DmdE3ltb+FIYMBZEhCqKDVCVS1JWkKS/M0Dno0pyMMZxNkPaK\nqCuuYHl1BStzPaVzimP4hnEN58WVBdx+YQ1rG6ev4TxWa1+aX+48wutHhjnYmyLrA8ZQ5qRYFOln\nbqaLsqEjRIaTRP0slYUxqsrLqayrp3LhIsoWLCYy4UP6ydaDrOeTHhoiFo8TjZ34ODvfGF5uHuCh\n3d1sbh7AdWDd/FLetaJy9ENfdngYHIdowfhleTjrs+dI0Jje1THE3q5hVtQmuG5JBZfOKz61BpEx\nuOk0+w91s/9wNw0RjwuLodwEy7CJxfAKCsiOTIUjt+OYiL3DPk51W/T0rtdZPXw/q0uO0F98EUNz\n3oOJjBkZxxgcYzCOc9IPCqls8AYdHGY2wL5cQ7Q0Dv/v6idYEm/lcfdDfO0Fn0vqi7nr6nq6jnSO\n236c6HjVSCQy2pguLS2ltLSUwsJT20sUi8VIJBIkEgkKCwtHp8mWvY6BDBsP97PxcB872oND5coL\nIlzeEPRMX1BXRCLqnnD5MZk01WVlHDly5MSFOc4x74+mP3n0/aDlMF5rE15/kmhZBW51LVTW4OS+\nUlUDZZXjfn935xA/2dLB9rZB6kpifPBNNVyzqAzXcfC9LNmhISKFhUSiZ3a4im8MnQPZ4Djq9PF7\nskem7Clc28AQ7JmcjtZW1IWo6570UvCGYM/UdLR7oy5EpnE7bWDSDymnI+Y6zC+Ps7axlDWNJSyu\nPPYkYGMMpFMQi49bpjw/+ED22Bs9bDrcj2dg0+ffBiFtQF8J3K21fkfu+y8CaK2/cYJfM7/8xCch\nFsfECxkuXUaqdAXZ+FwwPo7XhueWkiKO9rtJeEPcUJzkbfOyVNSWQWySlcn34EgHbkcbZAvYEVvA\nkx1ZDjpzeUe0BsdxSBuPjJ/F89MYP4WTHSKaHSCW7qcwk6Qg3YdrPIhEIRrBj0QhEsGPupjcV9+N\n4EV9PMeDiHe0TWVcHC+K60VxTJSsEydiPAr9FL4xeA74jjO6srkmGLDbdRyckZMZndx/ua++4zMU\nG8TgETcJXH/6djc5kFsJTTCZXNPQjN0gGDjesuQ442odvTn2/zELvGPAMT543ujkmKCIoB3kgPFx\nfYPvOuC4OG4wPCGuC66Dcd3cbRdziuu9j8E3Hj4+BgMOGNfgYHKP4Y9frXJZYhwc38HxXSJEiBDN\nHZPu4zs+Bh/j+PiOwYydN/HxAIwTPCYu+O7obcfxwM1gXG/051wTwfFcnGwkeE04+hIb14eIj+96\nGMc7+jzGBRPh5NuHkSx9ci/+KW5Sxv568NoEv+jmXi+HiAsuDg4OwcvsBK/hFB7+lEowgBcsL8a4\nY/72o5NDBIMLZHOTF0xOsAwYxwM3yN/Bx8lmcbI+rpfFzXo4noeT9YLvPe/oywwY18E4LsYNJhwH\nk1tGjePmlt1gclwwufuN6x697Rx92YNXMve/Q+5NwjnlnjfHOMR8h2jW4Ho+ZLOYbBbjpfEzGYyX\nxfGD5cu4Lk4kihON4UZjONE4TjSGE41iIhGyERffcTEjr58TfA1ey8jo/OD7qYU2suQZxm9fgm2O\nDyaD42dxTBb8LI6fOTp5wddYxAc3gk8cnChuJILrRnAiESJuFNeNBNsHAz4+3sga6Qdrv2dG1lCf\n0U1fjg94JlhSfANVhYMY4zCQiRLBYIw/WrmDi+s6uI6L67q4I8uGIfdzwZu7wQRfc7edMf9cxw1S\nd9yj83K5B7+fW1Zzz+obf8zrZ0YXnrHb3OB+Z/RvCdZPH9fzginj4fjBMh4s68Ey70yhvWAA341g\nosHy70ejmIiLibr4kQh+xIVxvbe5Csc8xcgy7uPiOcELF8HHzb12wOj23Yx7FEa2MIysI45z9LZN\nE19BM+Hr2OomVjr6tzkuJpfzmTznyO1Tec6ZMrGWibdPtTZjcm2Q3Aprcu2VyRbZo22O3FISrGB4\nOHzqcx+b7KkmNdunYjcAh8Z8fxhYO/GHlFKfAj4FoLXmnhXvP+aBSrMdLHcTnBeZQzEue4b38oW6\n3Vy/ZCcFkaPHNo3rFByrIjcBC80Wblo0l76B5fz3nna6smV4TjzY+EZiuNESIvEK4rhEHRcPmMr5\nrxEYbXuOmxkJZo305wwf5/d9IH0Kz+PmHt8DvPAcrnX6LA0UMNLkG2fiVm7iL+Ty9Amm4y12kPuA\nMPFxJzNZhhO3mZFcm/A4D+eOzDyTz8kn+tunYswL64+dN/brdJvQWWqMIcvIBF5uQxvBITpmmtgr\nPtowjuUaBjNU7kTHfF47w8dLR4AZHOUsm3t9PY5+na4uGoegEyHiOEEvXC6ryRpEx9uWjjNZYae4\n3Rz5CAYwmFsY3Ojkb/oj24RxJt3IjP/dkbxnelkbt4rklu/jn443847XoBpb52SvyTF9EZz5+hJq\n4TvCKu+EciwjrfUPgR/mvjVfunySYewMONkOsj2DRLMxrj9/CTCHVq4+/SeuhlXxDgYGe/EdFy8S\nfFL2XHf0ylSprMNA2iWZGQ56CnK9Czi5ngET9O64mTTRVIaoZ4jFSnEiDlFntC9uygyQzmYm7+X1\n/OAnjI/xfbIMM52bjkg02AXvuA5OrhdnOtddg8H4fq5XJteD40YgEvTm40YwkVyP8ugv+ZBJT/LR\n2oDv4Xg+jucHt/1TewuKuDHiTgyXM/v7fCCTzZ70507tsTw8J0MiUhT0Ojun11Mb9EwZPN877o6C\n2RY0Xb1cx/RIU2sai3McosUJIgVRiEVwYy5EnNGRdk5Ym+dj0j4m4+NlfLz+QZxohKwTwUQjZ3x8\nsOM4uPE4bnTMMdnGgJeFbG6vi+cdf68O4HseJpvF9070cW3M3wQMx11MLDZ+XZq0PnAwOI4Jdpw5\nQW+r5/mQyUI6E/QA4wd7R/DABHs6Rko2uT0OZyKS6yGK5LavkVicLGAiQQ6+M9KT7I7uZTBEgVjQ\nQzzmr3c8D9f3c69tFuck+7ZdHKKcWjvFxSMWc3BPa+t+LDPu32j/+yk72ld9tB92/OMT9Lkbg/Fz\nz+M4mHgME40HZ9UdRyQawcuepIntgBsvIDJm+R7pefeNwc9mYTiNyRy/a8hgwDcY4xF1XKIR94ze\ne3zPw89mMae4voSZG3Hxvdn6GH8WcHLLRiSK4wYXaDvV5cQ3ufaH72G8LLD6lJ92thvQTcDYQrCs\ndwAAE+hJREFUsYMac/NO6IoVV8xYQRMlauczM9eqO3vJGb/2SQb2SQb2SQb2SQb2SQbhMNsN6E3A\ncqXUYoKG8x3AB2a5BiGEEEIIIU7brB4pq7XOAn8OPAq8GszSr8xmDUIIIYQQQpyJWT8GWmv9MPDw\nbD+vEEIIIYQQ0+FcGKtBCCGEEEKIWSMNaCGEEEIIIaZAGtBCCCGEEEJMgTSghRBCCCGEmAJpQAsh\nhBBCCDEF0oAWQgghhBBiCqQBLYQQQgghxBRIA1oIIYQQQogpkAa0EEIIIYQQUyANaCGEEEIIIaZA\nGtBCCCGEEEJMgTSghRBCCCGEmAJpQAshhBBCCDEF0oAWQgghhBBiCqQBLYQQQgghxBRIA1oIIYQQ\nQogpkAa0EEIIIYQQU+AYY2zXcDKhL1AIIYQQQpwTnFP5odD3QCulXiL4Y2SyNEkG9ifJwP4kGdif\nJAP7k2Rgf5IMZnw6JaFvQAshhBBCCBEm0oAWQgghhBBiCs6GBvQPbRcgJIMQkAzskwzskwzskwzs\nkwxC4Gw4iVAIIYQQQojQOBt6oIUQQgghhAgNaUALIYQQQggxBVHbBYyllHKANUBDblYTsFFrLceZ\nzBLJwD7JwD7JwD7JwD7JwD7JILxCcwy0UuoG4AfAHoIFBKARWAb8mdb6t7ZqyxeSgX2SgX2SgX2S\ngX2SgX2SQbiFqQf6+8B1Wuv9Y2cqpRYDDwOrbBSVZyQD+yQD+yQD+yQD+yQD+ySDEAvTMdBR4PAk\n85uA2CzXkq8kA/skA/skA/skA/skA/skgxALUw/0j4BNSqmfA4dy8+YDdwD3Wqsqv0gG9kkG9kkG\n9kkG9kkG9kkGIRaaY6ABlFKrgZsZf7D8g1rrnfaqyi+SgX2SgX2SgX2SgX2SgX2SQXiFqgEthBBC\nCCFE2IXmEA6lVDnwReAWYA5ggHbgAeAerXWPxfLygmRgn2Rgn2Rgn2Rgn2Rgn2QQbmE6iVAD3cC1\nWusqrXU1sD43T1utLH9IBvZJBvZJBvZJBvZJBvZJBmFmjAnFdNttt712OvfJJBmcS5NkYH+SDOxP\nkoH9STKwP0kG4Z5CcwgHcEAp9QXgPq11G4BSqg74KEfPPhUzSzKwTzKwTzKwTzKwTzKwTzIIsTA1\noG8H7gI25BYQA7QBDwLKZmF5RDKwTzKwTzKwTzKwTzKwTzIIMRmFQwghhBBCiCkI00mEo5RSl57o\nezHzJAP7JAP7JAP7JAP7JAP7JIPwCWUDGvjTk3wvZp5kYJ9kYJ9kYJ9kYJ9kYJ9kEDJyCIcQQggh\nhBBTEKaTCAFQSl1OcK13D9ittd5luaS8opRaACS11j1KqUXA5cAurfUOu5XlF1kP7FNKxbTWmQnz\narTWnbZqymdKqT/TWv/Adh35RtaD8FBKlQDnAXvlIir2haYHWin1VuA7QA9wGfAcUAlkgA9prWXI\nlhmmlLoL+DSQAv4W+BxBDuuAe7XWf2exvLwg64F9Sqn1wL8DhcBm4FNa6/25+zZrreXYwxmmlPqL\nCbMcgiuy/Q2AbItmnqwH9imlfqC1/rPc7auBnwJvAMuAT2utH7ZZX74L0zHQ3wPeqbW+DrgUyGit\nrwK+DtxrtbL88SFgNXAV8F3gGq31HwNrgI/bLCyPyHpg37eAd2ita4AfAo8ppdbl7nPslZVXvgqs\nBUqA0tzXSO52qcW68omsB/atG3P7a8AtWuv1wFuB/9tOSWJEmBrQEa11R+72QWAhgNb6MaDBWlX5\nxdNaDxH0fg4BRwC01gNWq8ovsh7YF9davwKgtf7fwC3AfUqpWwjGYRUz73yC96di4Nta668C3Vrr\nr+Zui5kn60G4lGutNwNorfcSrvZbXgpTAC8qpe5VSn2QYDfFUwBKqSKCngcx8zYrpX4K/BfwBMHG\n8oNKqXuBnXZLyxuyHtiXUUrNHfkm14h4O3A3sNxWUflEa31Qa30b8DxBz+f7bNeUh2Q9sG+lUmqb\nUmo7sFwpVQmglHKBuN3SRJga0J8GXgKuBB4HPp+bb4B32Coqz3wC+DXwM4LDOf6ZII/XgI9ZrCuf\nyHpg311A3dgZWuvDwLXAPTYKylda6weAGwgO5zhsuZx8c7z14K3IejBbVgHvBm4CLgD6c/OrgK/Y\nKkoEQnMSoRBCCCGEOD6l1BytdbvtOkSIGtBKqXKCs6zfQ/Cp1wDtwAPAPTJky8wbk8EtBBn4SAah\noZR6RGv9Ttt1nOsmrAdzkG3RrMsdOvDXBNugrwB3Au8FdgGf0Vq3WCwvLyilygjWg0bgEa31T8fc\nNzo6hJg5SqmqCbMcgj2UlwCO1rpr9qsSI8I0DrQGngTWa61bYXQj+pHcfTdYrC1fjGRw7YQMPopk\nMCtOcHlWB7h4NmvJY8dbD2RbNHt+DDxEcBLh74D/BN5F8KHmnwk6WsTM+jdgD/BL4ONKqVuBD2it\nU4wfHULMnE7gwIR5DQTDChpgyaxXJEaFqQG9SGv9zbEzcm9e31RKyRBqs+N4GdyjlJJjoGfHJmAD\nkw8TVTHLteQr2RbZV6e1/gcYvYDKSB7/oJT6Y4t15ZOlWutbc7fvV0r9FfCkUupmm0Xlmc8D1wOf\n11pvB1BK7dNaL7ZbloBwNaAPKKW+ANyntW4DUErVEfR+ysUjZodkYN+rBAPk75l4h1JKMpgdsh7Y\nN/YE95+c4D4xcwqUUq7W2gfQWn9dKdUEPE0wLreYYVrr7yilfgF8N7f9/2tkCMHQCNOG6HagGtig\nlOpWSnURDOFVBSibheURycC+uzn+ennnLNaRz2Q9sO+B3GWL0Vp/aWSmUmoZsNtaVfnl18Dbxs7Q\nWv8Y+EsgbaOgfKS1Ppwb0vEp4DGgyG5FYkRoTiIUQgghhBCTU0olCA6t2WG7FhGuHuhRE0+kOsGJ\nVWKGSAb2SQb2SQb2SQb2SQb2KaUu1VoPjTSeJQP7QtmABv70JN+LmScZ2CcZ2CcZ2CcZ2CcZ2CcZ\nhIwcwiGEEEIIIcQUhGkUjnFyJ5CcB+yVCxfMDqVUHMhorU3u+/XApcBOrfUjVovLE5KBfUqpN2mt\nt9muI98ppRxgDcG4twBNwMaRdUPYo5RaqbXeZbuOfCYZ2BeaQziUUj8Yc/tqYCfwHWC7UuoPrBWW\nXzaRG2tYKfV54OtAAvgLpdQ9NgvLI5KBfS8rpfYopb6mlFptu5h8pJS6geAiHncDf5Cbvgrsyd0n\n7Pqt7QKEZGBbmHqgx17Z6GvALVrrzUqpJQRX/3rYTll5JaK17s7dvh24Rms9lGu4bQbuslda3pAM\n7NsGfAh4P/CgUmoA+Bnwc631fpuF5ZHvA9dNfL2VUosJ3gtW2Sgqnyil/v44dznIRZ1mhWQQbqHp\ngZ6gXGu9GUBrvZfw1nmuSSqlLsjd7gQKc7ejSAazRTKwz2itd2it/0prvQz4JDAHeFYp9bzl2vJF\nFDg8yfwmIDbLteSrjwE7gJcmTC8i40DPFskgxMLUA71SKbWN4JPVIqVUpda6WynlAnHLteWLPwH+\nUym1FWgHXlRKPQ1cCPyN1cryh2Rg37jLqGutNwIblVJ/CbzFTkl550fAJqXUzzl69cf5wB3Avdaq\nyi+bgB1a62M+NCql7p79cvKSZBBiYWpAT9wlN5D7WgV8ZZZryUta6225sSVvIDiBcytBL9Bn5UTO\n2SEZhMK3J5uZO3ltwyzXkpe01t9QSt0PvAe4Mje7Cfig1nqnvcryyvuA4cnu0FovnuVa8pVkEGIy\njJ0QQgghhBBTEJoeaKVUOfBF4BaC4w0NwS7sB4B7pPdt5kkG9kkG9kkG4aaUekRr/U7bdeQzycA+\nycC+0DSgCUbaeBK4VmvdCqCUmgt8JHefDF008yQD+yQD+yQDy05wmWIHuHg2a8lXkoF9kkG4hakB\nvUhr/c2xM3JvXt9USn3cUk35RjKwTzKwTzKwbxPB8ebOJPfJ8F2zQzKwTzIIsTA1oA8opb4A3Ke1\nbgNQStUBH+XoWdhiZkkG9kkG9kkG9r0KfFprvWfiHUopyWB2SAb2SQYhFqYG9O0EF4nYoJSak5vX\nBjwIKGtV5RfJwD7JwD7JwL67Of6453fOYh357G4kA9vuRjIILRmFQwghhBBCiCmQK5sJIYQQQggx\nBdKAFkIIIYQQYgqkAS2EEEIIIcQUnBUNaKXU9bZryHeSgX2SgX2SgX2SgX2SgX2SgX1nRQMauNd2\nAUIyCAHJwD7JwD7JwD7JwD7JwLLQDGOnlHrwOHc5QPVs1pKvJAP7JAP7JAP7JAP7JAP7JINwC00D\nGrgG+COgf8J8B1gz++XkJcnAPsnAPsnAPsnAPsnAPskgxMLUgP49MKi13jDxDqXUaxbqyUeSgX2S\ngX2SgX2SgX2SgX2SQYjJhVSEEEIIIYSYgrPlJEIhhBBCCCFCITSHcCil5gPfBhqAR4Bva60zufvu\n11rfYrO+fCAZ2CcZ2CcZ2CcZ2CcZ2CcZhFuYeqB/BDwF3AnUAxuUUiNnmS60VVSekQzskwzskwzs\nkwzskwzskwxCLDQ90ECt1vqfc7fvVEr9EfC0UupmQA7Unh2SgX2SgX2SgX2SgX2SgX2SQYiFqQc6\nppQqHPlGa/0fwGeARwk+eYmZJxnYJxnYJxnYJxnYJxnYJxmEWJga0P8KrB07Q2v9OHAbsMNKRflH\nMrBPMrBPMrBPMrBPMrBPMggxGcZOCCGEEEKIKQhTD/QxlFKbbdeQ7yQD+yQD+yQD+yQD+yQD+ySD\n8Ah1A5rgcpXCLsnAPsnAPsnAPsnAPsnAPskgJMLegH7IdgFCMggBycA+ycA+ycA+ycA+ySAkQnkM\ntFKqCkBr3WW7lnwlGdgnGdgnGdgnGdgnGdgnGYRPaBrQSqkFwLeAtwM9BLspyoAngbu01vvtVZcf\nJAP7JAP7JAP7JAP7JAP7JINwC9MhHL8AfgXM1Vov11ovIxjn8H7g51Yryx+SgX2SgX2SgX2SgX2S\ngX2SQZgZY0Ix3XbbbXtO5z6ZJINzaZIM7E+Sgf1JMrA/SQb2J8kg3FOYLuX9klLqB8B9wKHcvPnA\nR4CXrVWVXyQD+yQD+yQD+yQD+yQD+ySDEAtTA/rDwB8DXwUacvMOA78G7rVVVJ6RDOyTDOyTDOyT\nDOyTDOyTDEIsNCcRCiGEEEIIcTYI00mEx6WU+ortGvKdZGCfZGCfZGCfZGCfZGCfZGDfWdGABj5h\nuwAhGYSAZGCfZGCfZGCfZGCfZGBZaI6BVkolj3OXAyRms5Z8JRnYJxnYJxnYJxnYJxnYJxmEW2ga\n0ASDhF+htW6beIdS6tAkPy+mn2Rgn2Rgn2Rgn2Rgn2Rgn2QQYmE6hOMnwMLj3PfT2Swkj0kG9kkG\n9kkG9kkG9kkG9kkGISajcAghhBBCCDEFYeqBPoZS6m7bNeQ7ycA+ycA+ycA+ycA+ycA+ySA8Qt2A\nBm62XYCQDEJAMrBPMrBPMrBPMrBPMgiJsDegHdsFCMkgBCQD+yQD+yQD+yQD+ySDkAh7A/oy2wUI\nySAEJAP7JAP7JAP7JAP7JIOQCE0DWilVM+H7PwK+p5T6lFJKPnHNAsnAPsnAPsnAPsnAPsnAPskg\n3ELTgAZ+O3JDKfUl4EPAS8D1wN/ZKirPSAb2SQb2SQb2SQb2SQb2SQYhFqYG9NhPU+8F3qu1vg/4\nAHCdnZLyjmRgn2Rgn2Rgn2Rgn2Rgn2QQYmG6EmFCKXUJQaM+prUeANBaZ5RSnt3S8oZkYJ9kYJ9k\nYJ9kYJ9kYJ9kEGJh6oFuIdgl8bdAp1KqHkApVQ1kbRaWRyQD+yQD+yQD+yQD+yQD+ySDEAtND7TW\nev1x7uoB3jKbteQrycA+ycA+ycA+ycA+ycA+ySDcwtQDPSmttQcssF1HPpMM7JMM7JMM7JMM7JMM\n7JMMwiH0Deic3578R8QMkwzskwzskwzskwzskwzskwwsC80hHEqpvz/OXQ5QMZu15CvJwD7JwD7J\nwD7JwD7JwD7JINxC04AGPgb8JZCa5L73z3It+UoysE8ysE8ysE8ysE8ysE8yCLEwNaA3ATu01s9P\nvEMpdffsl5OXJAP7JAP7JAP7JAP7JAP7JIMQC1MD+n3A8GR3aK0Xz3It+UoysE8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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x110fd3610>"
+       "<Figure size 1200x900 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -3203,6 +1653,12 @@
    "cell_type": "code",
    "execution_count": 12,
    "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:54.949532Z",
+     "iopub.status.busy": "2026-09-05T22:08:54.949470Z",
+     "iopub.status.idle": "2026-09-05T22:08:55.017197Z",
+     "shell.execute_reply": "2026-09-05T22:08:55.016839Z"
+    },
     "scrolled": false
    },
    "outputs": [
@@ -3210,13 +1666,13 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "['304', '301', '302', '-', '405', '404', '401', '400', '501']\n"
+      "['302', '401', '501', '-', '404', '400', '405', '301', '304']\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x111531190>"
+       "<Axes: >"
       ]
      },
      "execution_count": 12,
@@ -3225,9 +1681,9 @@
     },
     {
      "data": {
-      "image/png": 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wk8C853nPua475Xney61LfgxMbfDcu4C7ADzPo1wuD77qhOqBxaRjr/uaK2YF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hhGtpcf1NZ5U8Rs9ST6xhbfyDCKZiZvI/TswvdzfFZSbRl94TLOodF9AFGQ6I+9WW4z/3+B1QTD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\nAABJRU5ErkJggg==\n",
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",
       "text/plain": [
-       "<matplotlib.figure.Figure at 0x104d01890>"
+       "<Figure size 1200x900 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -3239,7 +1695,7 @@
     "status_code_analysis2 = status_code_analysis\n",
     "if '200' in status_code_analysis2.keys():\n",
     "    del status_code_analysis2['200']\n",
-    "print(status_code_analysis2.keys())\n",
+    "print(list(status_code_analysis2.keys()))\n",
     "df2 = pd.DataFrame.from_dict(status_code_analysis2,orient='columns').fillna(0)\n",
     "df2.plot(rot=90, figsize=(12,9))"
    ]
@@ -3247,21 +1703,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.13"
+   "pygments_lexer": "ipython3",
+   "version": "3.14.7"
   }
  },
  "nbformat": 4,
diff --git a/Bro HTTP Log Analysis/Detecting Nmap Behavior with ParseZeekLogs.ipynb b/Bro HTTP Log Analysis/Detecting Nmap Behavior with ParseZeekLogs.ipynb
new file mode 100644
index 0000000..900729c
--- /dev/null
+++ b/Bro HTTP Log Analysis/Detecting Nmap Behavior with ParseZeekLogs.ipynb	
@@ -0,0 +1,686 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "671029a8",
+   "metadata": {},
+   "source": [
+    "# Detecting Nmap Behavior with Bro HTTP Logs, using ParseZeekLogs\n",
+    "\n",
+    "This is the hunt from [*Threat Hunting with Python: Prologue and Basic HTTP Hunting*](../docs/2017-09-threat-hunting-with-python-prologue.md) and [*Part 2: Detecting Nmap Behavior with Bro HTTP Logs*](../docs/2017-11-threat-hunting-with-python-part-2-nmap-bro-http.md), redone with today's tools. The two original notebooks in this folder split each line of `http.log` on tabs and count values by hand; they still run and are kept as written. Here the same questions are answered with [ParseZeekLogs](https://github.com/dgunter/ParseZeekLogs), which reads Bro/Zeek logs into typed records, and pandas.\n",
+    "\n",
+    "The log is one day of HTTP traffic from the 2015 4SICS conference lab network."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "2f1ef4c0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:47.775139Z",
+     "iopub.status.busy": "2026-09-05T22:11:47.774951Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.047053Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.046555Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from parsezeeklogs import ZeekLog\n",
+    "\n",
+    "plt.style.use(\"ggplot\")\n",
+    "%matplotlib inline"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b369e2fd",
+   "metadata": {},
+   "source": [
+    "## Load the log\n",
+    "\n",
+    "`ZeekLog` reads the `#fields` and `#types` header, so every column already has the right type: `ts` is a float, ports and status codes are integers, and unset fields (`-`) are `None`. No index arithmetic, no comment-line filtering."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "fd633f6b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.048439Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.048334Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.116864Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.116462Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "5758 records, 2015-10-21 10:09:20.958110094+00:00 to 2015-10-21 16:54:37.424937010+00:00\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>ts</th>\n",
+       "      <th>id.orig_h</th>\n",
+       "      <th>id.resp_h</th>\n",
+       "      <th>method</th>\n",
+       "      <th>host</th>\n",
+       "      <th>uri</th>\n",
+       "      <th>user_agent</th>\n",
+       "      <th>status_code</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2015-10-21 10:09:20.958470106+00:00</td>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.51</td>\n",
+       "      <td>GET</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>/</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2015-10-21 10:09:20.959613085+00:00</td>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.60</td>\n",
+       "      <td>GET</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>/</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>302.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2015-10-21 10:09:20.958110094+00:00</td>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.49</td>\n",
+       "      <td>GET</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>/</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>200.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2015-10-21 10:09:20.962326050+00:00</td>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.25</td>\n",
+       "      <td>GET</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>/</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>401.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2015-10-21 10:09:21.005907059+00:00</td>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.49</td>\n",
+       "      <td>OPTIONS</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>/</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>501.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                   ts     id.orig_h      id.resp_h   method  \\\n",
+       "0 2015-10-21 10:09:20.958470106+00:00  192.168.2.64  192.168.88.51      GET   \n",
+       "1 2015-10-21 10:09:20.959613085+00:00  192.168.2.64  192.168.88.60      GET   \n",
+       "2 2015-10-21 10:09:20.958110094+00:00  192.168.2.64  192.168.88.49      GET   \n",
+       "3 2015-10-21 10:09:20.962326050+00:00  192.168.2.64  192.168.88.25      GET   \n",
+       "4 2015-10-21 10:09:21.005907059+00:00  192.168.2.64  192.168.88.49  OPTIONS   \n",
+       "\n",
+       "  host uri user_agent  status_code  \n",
+       "0  NaN   /        NaN        200.0  \n",
+       "1  NaN   /        NaN        302.0  \n",
+       "2  NaN   /        NaN        200.0  \n",
+       "3  NaN   /        NaN        401.0  \n",
+       "4  NaN   /        NaN        501.0  "
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "with ZeekLog(\"http.log\") as log:\n",
+    "    df = pd.DataFrame(log)\n",
+    "\n",
+    "df[\"ts\"] = pd.to_datetime(df[\"ts\"], unit=\"s\", utc=True)\n",
+    "print(f\"{len(df)} records, {df['ts'].min()} to {df['ts'].max()}\")\n",
+    "df[[\"ts\", \"id.orig_h\", \"id.resp_h\", \"method\", \"host\", \"uri\", \"user_agent\", \"status_code\"]].head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "152a7311",
+   "metadata": {},
+   "source": [
+    "## Hypothesis 1: scanners leave fingerprints in the user agent and URI\n",
+    "\n",
+    "Stacking is counting values and looking at the outliers. Seven user agents cover the whole day, and one of them names the tool."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "24052b89",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.117949Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.117882Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.120515Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.120188Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "user_agent\n",
+       "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36    5045\n",
+       "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:39.0) Gecko/20100101 Firefox/39.0                                 327\n",
+       "Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)                               171\n",
+       "(unset)                                                                                                      103\n",
+       "Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:41.0) Gecko/20100101 Firefox/41.0                                  99\n",
+       "Mozilla/5.0 (X11; Ubuntu; Linux i686; rv:28.0) Gecko/20100101 Firefox/28.0                                    12\n",
+       "Wget/1.16.1 (linux-gnu)                                                                                        1\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[\"user_agent\"].fillna(\"(unset)\").value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "d58be78c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.121475Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.121426Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.125309Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.124989Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "uri\n",
+       "/                            187\n",
+       "/favicon.ico                  81\n",
+       "/stserial.asp                 80\n",
+       "/auth/auth.asp                23\n",
+       "/phoenix_fl.js                20\n",
+       "/phoenix_fl.css               20\n",
+       "/img/device_s.gif             20\n",
+       "/img/pxclogo.gif              20\n",
+       "/img/sel.gif                  16\n",
+       "/auth/topplan_auth.asp        15\n",
+       "/auth/name_auth.asp           13\n",
+       "/auth/led_auth.asp            13\n",
+       "/auth/accountpassword.asp     13\n",
+       "/auth/logo1.gif               13\n",
+       "/auth/logo3.gif               13\n",
+       "/auth/md5.js                  13\n",
+       "/auth/loginin.gif             13\n",
+       "/robots.txt                   11\n",
+       "/.git/HEAD                    11\n",
+       "/home.asp                     10\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Stack URIs with query strings removed\n",
+    "df[\"uri\"].str.split(\"?\").str[0].value_counts().head(20)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a881ea61",
+   "metadata": {},
+   "source": [
+    "## Search: who did the Nmap Scripting Engine talk to?\n",
+    "\n",
+    "The original notebook builds a nested dictionary of client, server and timestamp; a `groupby` does the same in one line."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "07d437df",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.126197Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.126149Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.130132Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.129858Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>id.orig_h</th>\n",
+       "      <th>id.resp_h</th>\n",
+       "      <th>requests</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>192.168.2.42</td>\n",
+       "      <td>192.168.88.115</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.51</td>\n",
+       "      <td>39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.25</td>\n",
+       "      <td>18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.60</td>\n",
+       "      <td>17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.95</td>\n",
+       "      <td>16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.115</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.61</td>\n",
+       "      <td>15</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.20</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.49</td>\n",
+       "      <td>14</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>192.168.2.64</td>\n",
+       "      <td>192.168.88.100</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      id.orig_h       id.resp_h  requests\n",
+       "0  192.168.2.42  192.168.88.115        18\n",
+       "6  192.168.2.64   192.168.88.51        39\n",
+       "4  192.168.2.64   192.168.88.25        18\n",
+       "7  192.168.2.64   192.168.88.60        17\n",
+       "9  192.168.2.64   192.168.88.95        16\n",
+       "2  192.168.2.64  192.168.88.115        15\n",
+       "8  192.168.2.64   192.168.88.61        15\n",
+       "3  192.168.2.64   192.168.88.20        14\n",
+       "5  192.168.2.64   192.168.88.49        14\n",
+       "1  192.168.2.64  192.168.88.100         5"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "NMAP_UA = \"Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)\"\n",
+    "nmap = df[df[\"user_agent\"] == NMAP_UA]\n",
+    "requests_per_pair = (\n",
+    "    nmap.groupby([\"id.orig_h\", \"id.resp_h\"]).size().rename(\"requests\").reset_index()\n",
+    ")\n",
+    "requests_per_pair.sort_values([\"id.orig_h\", \"requests\"], ascending=[True, False])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "476fb00a",
+   "metadata": {},
+   "source": [
+    "Two clients on the 192.168.2.0/24 network scanned nine hosts on 192.168.88.0/24. Every record involving those client/server pairs is worth a look, not just the ones with the Nmap user agent, since a scan is usually followed by something else."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "b05e8cce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.131023Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.130973Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.135049Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.134746Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "299 records between scanning clients and scanned servers\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "user_agent\n",
+       "Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html)                              171\n",
+       "(unset)                                                                                                      80\n",
+       "Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36     48\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pairs = set(map(tuple, requests_per_pair[[\"id.orig_h\", \"id.resp_h\"]].to_numpy()))\n",
+    "suspicious = df[[(c, s) in pairs for c, s in zip(df[\"id.orig_h\"], df[\"id.resp_h\"], strict=True)]]\n",
+    "print(f\"{len(suspicious)} records between scanning clients and scanned servers\")\n",
+    "suspicious[\"user_agent\"].fillna(\"(unset)\").value_counts()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "01f28152",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.136017Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.135957Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.139935Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.139508Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# Same CSV the original notebook produced, for people who want to pivot in a spreadsheet\n",
+    "suspicious.to_csv(\"suspicious_http_records_parsezeeklogs.csv\", index=False)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ee3fe855",
+   "metadata": {},
+   "source": [
+    "## Hypothesis 2: scanning shows up as bursts in time\n",
+    "\n",
+    "Part 2 of the series looks at the same log as a time series. Requests per minute per user agent make the scan window obvious once the high-volume browser is removed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "dd363dac",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.140769Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.140722Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.280294Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.279872Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "per_minute = (\n",
+    "    df.assign(minute=df[\"ts\"].dt.floor(\"min\"), user_agent=df[\"user_agent\"].fillna(\"(unset)\"))\n",
+    "    .groupby([\"minute\", \"user_agent\"])\n",
+    "    .size()\n",
+    "    .unstack(fill_value=0)\n",
+    ")\n",
+    "per_minute.plot(figsize=(12, 6), title=\"Requests per minute by user agent\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "332ba2d2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.281295Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.281234Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.392576Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.392174Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Drop the busiest user agent so the others are visible (when there is more than one)\n",
+    "busiest = per_minute.sum().idxmax()\n",
+    "rest = per_minute.drop(columns=busiest)\n",
+    "if not rest.empty:\n",
+    "    ax = rest.plot(figsize=(12, 6))\n",
+    "    ax.set_title(f\"Requests per minute by user agent, without {busiest[:40]}...\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb17400a",
+   "metadata": {},
+   "source": [
+    "## Status codes\n",
+    "\n",
+    "A scanner probing paths that do not exist produces error responses in bulk. The distribution and the per-minute view both show it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "477f37ce",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.393526Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.393468Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.441295Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.440860Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 700x700 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "status = df[\"status_code\"].fillna(0).astype(int).value_counts().sort_index()\n",
+    "labels = [f\"{code} ({count})\" for code, count in status.items()]\n",
+    "fig, ax = plt.subplots(figsize=(7, 7))\n",
+    "patches, _ = ax.pie(status, startangle=90)\n",
+    "ax.legend(patches, labels, loc=\"best\")\n",
+    "ax.set_title(\"Status code distribution (0 = no response logged)\")\n",
+    "ax.axis(\"equal\");"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "e05ba56b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:11:48.442262Z",
+     "iopub.status.busy": "2026-09-05T22:11:48.442202Z",
+     "iopub.status.idle": "2026-09-05T22:11:48.516050Z",
+     "shell.execute_reply": "2026-09-05T22:11:48.515698Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "status_per_minute = (\n",
+    "    df.assign(minute=df[\"ts\"].dt.floor(\"min\"), status=df[\"status_code\"].fillna(0).astype(int))\n",
+    "    .groupby([\"minute\", \"status\"])\n",
+    "    .size()\n",
+    "    .unstack(fill_value=0)\n",
+    ")\n",
+    "status_per_minute.drop(columns=200, errors=\"ignore\").plot(\n",
+    "    figsize=(12, 6), title=\"Non-200 responses per minute\"\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "73bc2c23",
+   "metadata": {},
+   "source": [
+    "## What changed since 2017\n",
+    "\n",
+    "- The log is parsed by a library that understands the Zeek format, so the notebook is about the hunt rather than about tab positions.\n",
+    "- Timestamps are real datetimes; the original stringified them minute by minute to group them.\n",
+    "- `groupby` and `value_counts` replace the hand-rolled dictionaries.\n",
+    "- The same library can also emit these records in Elastic Common Schema for Elasticsearch or Kibana, if the hunt outgrows a notebook."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.14.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Bro HTTP Log Analysis/suspicious_http_records_parsezeeklogs.csv b/Bro HTTP Log Analysis/suspicious_http_records_parsezeeklogs.csv
new file mode 100644
index 0000000..46f3747
--- /dev/null
+++ b/Bro HTTP Log Analysis/suspicious_http_records_parsezeeklogs.csv	
@@ -0,0 +1,300 @@
+ts,uid,id.orig_h,id.orig_p,id.resp_h,id.resp_p,trans_depth,method,host,uri,referrer,user_agent,request_body_len,response_body_len,status_code,status_msg,info_code,info_msg,filename,tags,username,password,proxied,orig_fuids,orig_mime_types,resp_fuids,resp_mime_types
+2015-10-21 10:09:20.958470106+00:00,CTLRyA2VZnI0G0Ioda,192.168.2.64,51827,192.168.88.51,80,1,GET,,/,,,0,2467,200.0,OK,,,,[],,,,,,['F0tNl28XAlwChFpvc'],['text/html']
+2015-10-21 10:09:20.959613085+00:00,CojNPR3SMW53I6b9c7,192.168.2.64,51834,192.168.88.60,80,1,GET,,/,,,0,254,302.0,Redirect,,,,[],,,,,,['F4g28g4KbIf9S4C3D8'],['text/html']
+2015-10-21 10:09:20.958110094+00:00,CERnSV1KTUPoSLBT2k,192.168.2.64,51824,192.168.88.49,80,1,GET,,/,,,0,714,200.0,OK,,,,[],,,,,,['FeYOGC1mLh5bUFVblb'],['text/html']
+2015-10-21 10:09:20.962326050+00:00,CJgX1z3hG0NWfniAxh,192.168.2.64,51821,192.168.88.25,80,1,GET,,/,,,0,0,401.0,Not authorized,,,,[],,,,,,,
+2015-10-21 10:09:21.005907059+00:00,C5bfS73u3gyLjp2H,192.168.2.64,51866,192.168.88.49,80,1,OPTIONS,,/,,,0,148,501.0,Not Implemented,,,,[],,,,,,['FXcukj3ZXni7wrKBQk'],['text/html']
+2015-10-21 10:09:21.015944004+00:00,CeKyDY1iLVxpo0H3lk,192.168.2.64,51865,192.168.88.25,80,1,OPTIONS,,/,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:21.048046112+00:00,Cig0dO2RyXlM1auQb2,192.168.2.64,51868,192.168.88.25,80,1,OPTIONS,,/ RTSP/1.,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:21.067540884+00:00,CpDtFi452cbzBwnm79,192.168.2.64,51848,192.168.88.95,80,1,GET,,/,,,0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 10:09:21.971020937+00:00,CmkQf92ndfptNIRv0g,192.168.2.64,51840,192.168.88.61,80,1,GET,,/,,,0,254,302.0,Redirect,,,,[],,,,,,['FbLKi63AJKuVuwNpX4'],['text/html']
+2015-10-21 10:09:25.973754883+00:00,CwmdH63TccpEdTOxme,192.168.2.64,51884,192.168.88.51,5120,1,GET,,/,,,0,2467,200.0,OK,,,,[],,,,,,['FE1wg53KDcftda4L1'],['text/html']
+2015-10-21 10:09:26.094773054+00:00,CyzaFd4LnRv0T4H2r1,192.168.2.64,51890,192.168.88.25,80,1,GET,,"/nice ports,/Trinity.txt.bak",,,0,0,401.0,Not authorized,,,,[],,,,,,,
+2015-10-21 10:09:26.330199003+00:00,CZQrRD26NjVCsNhBCd,192.168.2.64,51892,192.168.88.25,80,1,,,,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:26.350697041+00:00,C1DfHtw6cwDmzbZhd,192.168.2.64,51855,192.168.88.115,80,1,GET,,/,,,0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 10:09:24.185045958+00:00,C1CBxM1wcplvqY1lnj,192.168.2.64,51852,192.168.88.100,80,1,GET,,/,,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:29.190304041+00:00,Cn0A1j4Aiq9gIQ5hyk,192.168.2.64,51904,192.168.88.100,80,1,OPTIONS,,/,,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:40.762037992+00:00,CXscW3dzDXXsOoU91,192.168.2.64,51825,192.168.88.49,49152,1,GET,,"/nice ports,/Trinity.txt.bak",,,0,48,404.0,Not Found,,,,[],,,,,,['FKKNDO3x3PISKu5Ml'],['text/html']
+2015-10-21 10:09:40.767008066+00:00,Cljxky2x7iBkD79KZ,192.168.2.64,51912,192.168.88.49,49152,1,GET,,/,,,0,48,404.0,Not Found,,,,[],,,,,,['FFTwot1gPTSWhZ9yvk'],['text/html']
+2015-10-21 10:09:40.769318104+00:00,CmOMIj19go2r58DE5h,192.168.2.64,51928,192.168.88.49,49152,1,OPTIONS,,/,,,0,54,501.0,Not Implemented,,,,[],,,,,,['FuoT4G3J8ps3b2S5Xd'],['text/html']
+2015-10-21 10:09:41.406742096+00:00,CObEoL3P7KJu1GiqMg,192.168.2.64,51950,192.168.88.25,80,1,,,,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:41.439820051+00:00,CfaK3LpJhGWdAxLye,192.168.2.64,51951,192.168.88.25,80,1,,,,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:41.472541094+00:00,CGOZLe1bBAG4TEs9zf,192.168.2.64,51952,192.168.88.25,80,1,,,,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:34.196861982+00:00,C0Yyks2bOudLBgNXz,192.168.2.64,51927,192.168.88.100,80,1,OPTIONS,,/ RTSP/1.,,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:46.529325008+00:00,CN8OZt3x1cJUfc8WH9,192.168.2.64,51966,192.168.88.25,80,1,,,,,,0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:09:44.208637953+00:00,CUTe7d4rHNYiV7KZF8,192.168.2.64,51959,192.168.88.100,80,1,GET,,"/nice ports,/Trinity.txt.bak",,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:51.567401886+00:00,Co3pRC4aYpMbYBP4Ok,192.168.2.64,51978,192.168.88.25,80,1,OPTIONS,,sip:nm SIP/2.,,,0,0,501.0,Not implemented,,,,[],,,['VIA -> SIP/2.0/TCP nm;branch=foo'],,,,
+2015-10-21 10:10:36.756478071+00:00,C7UzSW1y9y0aCxJQ8k,192.168.2.64,52073,192.168.88.100,80,1,OPTIONS,,sip:nm SIP/2.,,,0,0,,,,,,[],,,['VIA -> SIP/2.0/TCP nm;branch=foo'],,,,
+2015-10-21 10:11:29.381937981+00:00,CsJfa63Tm2AjMmm2pe,192.168.2.64,52159,192.168.88.51,5120,1,GET,192.168.88.51,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,149,401.0,Unauthorized,,,,[],,,,,,['F8717y1CteE1yorTXe'],['text/html']
+2015-10-21 10:11:29.385586023+00:00,CJqYWywK4tywLkXGj,192.168.2.64,52169,192.168.88.51,5120,1,GET,192.168.88.51,/master.jsp,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,95,404.0,Object Not Found,,,,[],,,,,,['F4VKsG41cnBixMYdn4'],['text/html']
+2015-10-21 10:11:29.380561113+00:00,CQ62S72onQ858yc71l,192.168.2.64,52168,192.168.88.49,80,1,GET,192.168.88.49,/robots.txt,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,26,200.0,OK,,,,[],,,,,,['FWkDAp3u3X8V7n0eK4'],['text/plain']
+2015-10-21 10:11:29.381463051+00:00,CUmIvM3effTpOUucJ6,192.168.2.64,52174,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:29.591706038+00:00,CaYzfG4bWqvzDlnxVa,192.168.2.64,52182,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:29.379890919+00:00,CzZOhq1dAOLfrMH4Ch,192.168.2.64,52160,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FfJxyy47XPJ4JQRwHc'],['text/html']
+2015-10-21 10:11:29.592097998+00:00,CbEN9L1Jy6KQy3k3w9,192.168.2.64,52187,192.168.88.51,80,1,GET,192.168.88.51,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,2467,200.0,OK,,,,[],,,,,,['FRopbF3EY0eKVKsvQe'],['text/html']
+2015-10-21 10:11:29.381131887+00:00,CAI5Nm2NPJooC3NH8,192.168.2.64,52172,192.168.88.60,80,1,GET,192.168.88.60,/.git/HEAD,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,203,302.0,Redirect,,,,[],,,,,,['FbDNUp41VybgAG01Ka'],['text/html']
+2015-10-21 10:11:29.591833115+00:00,CPKqwJ22XGqgY8kOmd,192.168.2.64,52183,192.168.88.61,80,1,GET,192.168.88.61,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,203,302.0,Redirect,,,,[],,,,,,['FTslGf4WdCa10QdMTj'],['text/html']
+2015-10-21 10:11:29.381937981+00:00,CCuSlb17rm9pK90nwg,192.168.2.64,52176,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FE2P1v3ox5kLWAFNF9'],['text/html']
+2015-10-21 10:11:29.591967106+00:00,CUc2652gzOYZzFlGIk,192.168.2.64,52184,192.168.88.60,80,1,GET,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,203,302.0,Redirect,,,,[],,,,,,['FTgjHp4G4vHZ0ipmR5'],['text/html']
+2015-10-21 10:11:29.754807949+00:00,CtsGRX2b6EgImMFJ1f,192.168.2.64,52191,192.168.88.51,80,1,GET,192.168.88.51,/favicon.ico,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,95,404.0,Object Not Found,,,,[],,,,,,['F6D8mC3pc661RDRTO3'],['text/html']
+2015-10-21 10:11:29.808331966+00:00,CvgE5WjdvdOhWwQqf,192.168.2.64,52194,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:29.866199017+00:00,CyRpGM28WbOwiPAmXc,192.168.2.64,52199,192.168.88.61,80,1,GET,192.168.88.61,/auth/auth.asp,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,806,200.0,OK,,,,[],,,,,,['FbJDG7WucDDK5jPFg'],['text/html']
+2015-10-21 10:11:30.020237923+00:00,Cee8da4E7JwBtGWBN3,192.168.2.64,52206,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:29.865632057+00:00,CfVfQs3c6AX2T8z6j5,192.168.2.64,52196,192.168.88.60,80,1,GET,192.168.88.60,/auth/auth.asp,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,817,200.0,OK,,,,[],,,,,,['Fslv0R2EfhIQhqkuod'],['text/html']
+2015-10-21 10:11:30.239120007+00:00,CVBP5B2AwMUu0lytJ5,192.168.2.64,52214,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:30.239258051+00:00,CusqF5328WpN3x6Gr3,192.168.2.64,52215,192.168.88.25,80,1,GET,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,401.0,Not authorized,,,,[],,,,,,,
+2015-10-21 10:11:30.349694014+00:00,CjYhN016rhA3R2L9X4,192.168.2.64,52220,192.168.88.51,5120,1,GET,192.168.88.51,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,2467,200.0,OK,,,,[],,,,,,['F6qph21efu9KjBlSG2'],['text/html']
+2015-10-21 10:11:29.865921021+00:00,ClKzSr4XmFARZTHbN9,192.168.2.64,52198,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FofJ2e47wnZqYwl0p2'],['text/html']
+2015-10-21 10:11:30.459929943+00:00,CD9rto31HlI0fXdzaa,192.168.2.64,52221,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:30.520663977+00:00,CWQKRkQkcwpwXi3Ac,192.168.2.64,52223,192.168.88.51,5120,1,GET,192.168.88.51,/favicon.ico,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,95,404.0,Object Not Found,,,,[],,,,,,['FVhbIi3Arlh18Sg3eb'],['text/html']
+2015-10-21 10:11:30.513639927+00:00,Cp32Sc2mSJwO7LRfdc,192.168.2.64,52224,192.168.88.100,80,1,GET,192.168.88.100,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,1461,200.0,OK,,,,[],,,,,,['FOkqwTOmmIVhjOg37'],['text/html']
+2015-10-21 10:11:30.678546906+00:00,C3UUpNR9sgFgfqvIe,192.168.2.64,52232,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:30.565864086+00:00,CkFVDg5XVhwGCNI1,192.168.2.64,52227,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FjJul7ou510OYgerh'],['text/html']
+2015-10-21 10:11:30.915766954+00:00,CjkROE2rNDVGUr2xw1,192.168.2.64,52238,192.168.88.61,80,1,GET,192.168.88.61,/favicon.ico,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,203,302.0,Redirect,,,,[],,,,,,['FHbLNd1NRureDK2Qd7'],['text/html']
+2015-10-21 10:11:30.967679024+00:00,CZzD654aCsfMr0Wm48,192.168.2.64,52236,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:31.018922091+00:00,C321aq4iM16fG7ohWl,192.168.2.64,52243,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['F5zo0310yykTl2zMP1'],['text/html']
+2015-10-21 10:11:31.184860945+00:00,CJOBEk2ntg4JOtCt8f,192.168.2.64,52245,192.168.88.25,80,1,OPTIONS,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,501.0,Not implemented,,,,[],,,,,,,
+2015-10-21 10:11:31.185003996+00:00,CMORvE1hZpiUzlgMLi,192.168.2.64,52246,192.168.88.25,80,1,GET,192.168.88.25,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,401.0,Not authorized,,,,[],,,,,,,
+2015-10-21 10:11:31.235169888+00:00,CfnoeTuNfkacQn0m5,192.168.2.64,52247,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FIQd2P1JsMWpLcHvJk'],['text/html']
+2015-10-21 10:11:30.968135118+00:00,CbjFrzoGhgvdbOKId,192.168.2.64,52240,192.168.88.20,80,1,GET,192.168.88.20,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,3817,200.0,OK,,,,[],,,,,,['FG1kq43bhmHIpzrvl9'],['text/html']
+2015-10-21 10:11:31.619374990+00:00,Cdp2F4jirPEIdSUV,192.168.2.64,52255,192.168.88.49,80,1,GET,192.168.88.49,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,714,200.0,OK,,,,[],,,,,,['FRxmtbsykbcg2Inlg'],['text/html']
+2015-10-21 10:11:31.454247952+00:00,CYMbRc32m6oD5Shv64,192.168.2.64,52250,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['F3mfGC3GErWZGJP6Wi'],['text/html']
+2015-10-21 10:11:31.454376936+00:00,CHl8zC3D9UDxMxoCrh,192.168.2.64,52251,192.168.88.115,80,1,GET,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,200,302.0,Redirect,,,,[],,,,,,['FyVPeA4JegK7jSKzNk'],['text/html']
+2015-10-21 10:11:31.885204077+00:00,CFHDbscCnXAbJTg4d,192.168.2.64,52260,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FgCGgF3Eq2iF8BLF3b'],['text/html']
+2015-10-21 10:11:31.885333061+00:00,CMjFI52HlUdMZYnveg,192.168.2.64,52261,192.168.88.20,80,1,GET,192.168.88.20,/favicon.ico,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,2998,200.0,OK,,,,[],,,,,,['FUR55m2te2ZJIFLUOl'],['image/x-icon']
+2015-10-21 10:11:31.832760096+00:00,C3BxJw2F16tIo2F9Ia,192.168.2.64,52257,192.168.88.100,80,1,GET,,/,,,0,1461,200.0,OK,,,,[],,,,,,['FwY8a72RfDHu9wDvV2'],['text/html']
+2015-10-21 10:11:32.047761917+00:00,CWSHIF3DtJZU1g1a2h,192.168.2.64,52266,192.168.88.49,80,1,GET,192.168.88.49,/favicon.ico,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,153,404.0,Not Found,,,,[],,,,,,['FyrTgAhYbCrAqNmwg'],['text/html']
+2015-10-21 10:11:32.100842953+00:00,CPfsrOZ7cCBXYemOe,192.168.2.64,52267,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FOdKc34eTT6ncEcFl6'],['text/html']
+2015-10-21 10:11:31.938518047+00:00,CoRXn81s8q5GYL1Dn4,192.168.2.64,52262,192.168.88.115,80,1,GET,192.168.88.115,/login.asp,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,2325,200.0,OK,,,,[],,,,,,['F2U4s5gTWsgvHQn6h'],['text/html']
+2015-10-21 10:11:32.319808006+00:00,CbA7Wl4L9NUzgkvYn9,192.168.2.64,52272,192.168.88.60,80,1,OPTIONS,192.168.88.60,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['Fbi1GL3XDRsCrgM53h'],['text/html']
+2015-10-21 10:11:32.476079941+00:00,CUm5VeacXeBZO6Rac,192.168.2.64,52279,192.168.88.51,80,1,GET,192.168.88.51,/.git/HEAD,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,95,404.0,Object Not Found,,,,[],,,,,,['F5lO0lp133V6482K6'],['text/html']
+2015-10-21 10:11:32.423562050+00:00,C2JpXd1zR7s5f5dWEg,192.168.2.64,52277,192.168.88.100,80,1,GET,,/,,,0,1461,200.0,OK,,,,[],,,,,,['F8uclz2Ai6Wp80Jv7'],['text/html']
+2015-10-21 10:11:32.587507963+00:00,CmboUx49TQCD0cl8ma,192.168.2.64,52282,192.168.88.51,5120,1,GET,192.168.88.51,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,2467,200.0,OK,,,,[],,,,,,['FSAUO43HtvjLR5Bk2h'],['text/html']
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+2015-10-21 10:12:00.387407064+00:00,CopSSo1CyrPc7Zdbyh,192.168.2.64,52601,192.168.88.95,80,1,OPTIONS,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 10:12:28.751231909+00:00,CGjxpE433SWqQJGHGk,192.168.2.64,52649,192.168.88.95,80,1,OPTIONS,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FYLWk33NH5NaseU0n8'],['text/html']
+2015-10-21 10:12:32.728655100+00:00,CdcEIa4amNg2NCd7w8,192.168.2.64,52584,192.168.88.95,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 10:11:49.529967070+00:00,C0PGJ7let7VCylwwb,192.168.2.64,52529,192.168.88.95,80,1,OPTIONS,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 10:11:41.580724001+00:00,CBVj69kFALXpuEd6h,192.168.2.64,52459,192.168.88.95,80,1,GET,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 10:12:32.814637899+00:00,CkNJiO1RlTeCgby3pj,192.168.2.64,52458,192.168.88.95,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 10:12:32.905376911+00:00,Csn1xO2m8J7kAvfZa2,192.168.2.64,52652,192.168.88.95,80,1,OPTIONS,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,156,400.0,Page not found,,,,[],,,,,,['FNkNML2n9JuUEL7r69'],['text/html']
+2015-10-21 10:11:35.370558977+00:00,CldwHv1TAGu5kz664i,192.168.2.64,52343,192.168.88.95,80,1,OPTIONS,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 10:11:30.347162008+00:00,CP9Wvo19SblBKLlyLf,192.168.2.64,52218,192.168.88.95,80,1,GET,192.168.88.95,/robots.txt,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,405.0,Access Denied,,,,[],,,,,,,
+2015-10-21 10:11:29.380290031+00:00,CXXU0u1K8tIP1sPFe8,192.168.2.64,52163,192.168.88.95,80,1,GET,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 10:11:29.380168915+00:00,CQaP061hSl7jOJLF3g,192.168.2.64,52162,192.168.88.95,80,1,OPTIONS,192.168.88.95,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 10:09:20.969315052+00:00,CwFOwH1Rfku1KZxje,192.168.2.64,51822,192.168.88.25,81,1,GET,,/,,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:25.988980055+00:00,CeATwU1HNqHtAgXeO8,192.168.2.64,51888,192.168.88.25,81,1,OPTIONS,,/,,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:36.011574984+00:00,CzkCMX39S5OQRg6702,192.168.2.64,51933,192.168.88.25,81,1,GET,,"/nice ports,/Trinity.txt.bak",,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:09:46.032051086+00:00,CGF2DT3QBKJKFk2ne7,192.168.2.64,51965,192.168.88.25,81,1,OPTIONS,,/ RTSP/1.,,,0,0,,,,,,[],,,,,,,
+2015-10-21 10:10:38.667670012+00:00,CGBCtvGhOKTgrKMkd,192.168.2.64,52081,192.168.88.25,81,1,OPTIONS,,sip:nm SIP/2.,,,0,0,,,,,,[],,,['VIA -> SIP/2.0/TCP nm;branch=foo'],,,,
+2015-10-21 11:03:54.728235006+00:00,CHE9LZ3HvLdTELGrO1,192.168.2.42,46212,192.168.88.115,80,1,GET,,/,,,0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:04:16.491738081+00:00,CWLWPI2rfFrdVMSAR7,192.168.2.42,46222,192.168.88.115,80,1,GET,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:04:16.491857052+00:00,CGbR9140yyP4hrsHki,192.168.2.42,46224,192.168.88.115,80,1,GET,,/,,,0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:04:16.492151976+00:00,C4GYlQVabxN9xB7P2,192.168.2.42,46228,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:16.491595984+00:00,Czcear2Qg8wpiaRMu,192.168.2.42,46231,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.439130068+00:00,CpIt6A4XQCbbQ9Ity4,192.168.2.42,46255,192.168.88.115,80,1,,,,,,0,156,400.0,Page not found,,,,[],,,,,,['FCZaE6T5UTiOhiuJh'],['text/html']
+2015-10-21 11:05:29.451067924+00:00,Cqdki54DZMX7cXKXe8,192.168.2.42,46256,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.461726904+00:00,CHByCk3M5g6MY9xp3g,192.168.2.42,46257,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.472600937+00:00,CQp70u1IIRtbKGxp1i,192.168.2.42,46258,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.483016968+00:00,CN1Y161iSGiUTaSvZg,192.168.2.42,46259,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.494216919+00:00,CseypScYFqN1s0G1f,192.168.2.42,46260,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:24.684730053+00:00,CA5Ivm4klpH2CSlWv9,192.168.2.42,46263,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:24.734433889+00:00,CM4FcL11LiheBnfsij,192.168.2.42,46265,192.168.88.115,80,1,GET,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:05:29.555304050+00:00,CkhNRr4x3ZCyyRYjj5,192.168.2.42,46268,192.168.88.115,80,1,,,,,,0,156,400.0,Page not found,,,,[],,,,,,['FvzDg92OWlOLL7OBJd'],['text/html']
+2015-10-21 11:05:29.567841053+00:00,Cb2J4X3x6h82iRo9u7,192.168.2.42,46269,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.579684019+00:00,CsrtPs3qmJb8uqn8rf,192.168.2.42,46270,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:32.798104048+00:00,Cxi9VkQjUE1MsNbs2,192.168.2.42,46273,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:32.897109985+00:00,CSU0Xv4EaBSq75rGaa,192.168.2.42,46274,192.168.88.115,80,1,GET,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:05:29.629987001+00:00,CYlPVt1gopceVJseIe,192.168.2.42,46277,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.640933037+00:00,CNM6gc1QdQDPsFugCg,192.168.2.42,46278,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:40.908742905+00:00,Cu6M4i4J578Ehi01Dj,192.168.2.42,46281,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:41.058994055+00:00,CtE0Dg1EmI7mpdvdc,192.168.2.42,46282,192.168.88.115,80,1,GET,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:05:29.690819979+00:00,CE9CFk2btG7RBo53Wl,192.168.2.42,46286,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.701559067+00:00,Cx9eroUHwqF1TIim2,192.168.2.42,46287,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:49.066291093+00:00,COr4Tl43sIxZpdGPvj,192.168.2.42,46290,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:49.264708041+00:00,CpB3zat6DdUTwQGOb,192.168.2.42,46291,192.168.88.115,80,1,GET,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,302.0,Redirect,,,,[],,,,,,,
+2015-10-21 11:05:29.756283045+00:00,CRjgnm3Mpbf7YgLkVf,192.168.2.42,46298,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.767046928+00:00,CX3lo31LtL5TRqrpbl,192.168.2.42,46299,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:57.221008062+00:00,CVJPSx4MUDUnrbXmk,192.168.2.42,46303,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.794598103+00:00,CyMwXXd02LTGNkRCl,192.168.2.42,46307,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:05.330748081+00:00,CurQiu44YjI4gN7qf1,192.168.2.42,46310,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.829005003+00:00,C9VoenzaluvaeP4Lc,192.168.2.42,46314,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:13.438493013+00:00,C3dujP2qAdk7yStjh2,192.168.2.42,46316,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.850655079+00:00,CRNgcE4UecP7zY2mx,192.168.2.42,46324,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:21.550030947+00:00,CBPtV9ui8y0wwGq8h,192.168.2.42,46328,192.168.88.115,80,1,OPTIONS,192.168.88.115,/,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:05:29.876605988+00:00,CYzaELdC5OMeU6Sl3,192.168.2.42,46330,192.168.88.115,80,1,,,,,,0,0,400.0,Page not found,,,,[],,,,,,,
+2015-10-21 11:04:16.492018938+00:00,CTf2er24tvmEGI592,192.168.2.42,46236,192.168.88.115,80,1,GET,192.168.88.115,/robots.txt,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,,,,,,[],,,,,,,
+2015-10-21 11:04:16.492557049+00:00,CTm2aV30s6jtWvq9Ci,192.168.2.42,46239,192.168.88.115,80,1,GET,192.168.88.115,/.git/HEAD,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,,,,,,[],,,,,,,
+2015-10-21 11:04:24.684854031+00:00,CF9lOq2Jg6S5DLgqRb,192.168.2.42,46264,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,,Mozilla/5.0 (compatible; Nmap Scripting Engine; http://nmap.org/book/nse.html),0,0,,,,,,[],,,,,,,
+2015-10-21 11:09:55.438998938+00:00,CgQt8ZXtCQIuuqL45,192.168.2.42,51071,192.168.88.115,80,1,GET,192.168.88.115,/,,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,200,302.0,Redirect,,,,[],,,,,,['FNI1Ap2KOKVQ41Wf09'],['text/html']
+2015-10-21 11:09:55.456743956+00:00,Cv0TsP3XYtbeESds17,192.168.2.42,51072,192.168.88.115,80,1,GET,192.168.88.115,/login.asp,,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,2325,200.0,OK,,,,[],,,,,,['F4qHdJ3QAlpwO5T9ki'],['text/html']
+2015-10-21 11:09:55.640414+00:00,CYwQNw3AGpLJWO5cIh,192.168.2.42,51074,192.168.88.115,80,1,GET,192.168.88.115,/jscript/default.js,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1566,200.0,OK,,,,[],,,,,,['F4T67H04AJwKuKuf7'],['text/plain']
+2015-10-21 11:09:55.641020060+00:00,CphfcF4P32nadKm8e,192.168.2.42,51075,192.168.88.115,80,1,GET,192.168.88.115,/jscript/slidemenu.js,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,10307,200.0,OK,,,,[],,,,,,['FyHzHCtkHzZkBbaih'],['text/plain']
+2015-10-21 11:09:55.641597986+00:00,CIB2ir19DLEEe2cC7b,192.168.2.42,51076,192.168.88.115,80,1,GET,192.168.88.115,/jscript/login.js,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,592,200.0,OK,,,,[],,,,,,['FbbEW94V3uOM1vjQI6'],['text/plain']
+2015-10-21 11:09:55.642218113+00:00,CPKu6Y11AyKEHBiVck,192.168.2.42,51077,192.168.88.115,80,1,GET,192.168.88.115,/jscript/util.js,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1959,200.0,OK,,,,[],,,,,,['Fuhk6e4xLkFxkB3uZd'],['text/plain']
+2015-10-21 11:09:55.642995119+00:00,CyKwA1YDJcooED5l1,192.168.2.42,51078,192.168.88.115,80,1,GET,192.168.88.115,/jscript/validation.js,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,3390,200.0,OK,,,,[],,,,,,['FBMti11JNwha5aylgl'],['text/plain']
+2015-10-21 11:09:55.643724918+00:00,CNIcHc4iZdNSnCNXH8,192.168.2.42,51079,192.168.88.115,80,1,GET,192.168.88.115,/images/logo.gif,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,2560,200.0,OK,,,,[],,,,,,['FakvZa48LJlPbpC7za'],['image/gif']
+2015-10-21 11:09:55.683077097+00:00,CajrTC24HKwdpceVra,192.168.2.42,51080,192.168.88.115,80,1,GET,192.168.88.115,/css/digistyle.css,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1858,200.0,OK,,,,[],,,,,,['FC5o9l1dEI1MJNYDZ5'],['text/plain']
+2015-10-21 11:09:55.879966021+00:00,CWRVKH2SB9k85z7TE1,192.168.2.42,51081,192.168.88.115,80,1,GET,192.168.88.115,/images/ws_button3.gif,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1359,200.0,OK,,,,[],,,,,,['FWgB1AQShJrZ7Yo6e'],['image/gif']
+2015-10-21 11:10:00.648303986+00:00,C6VDCKuufs2eNAOAe,192.168.2.42,51083,192.168.88.115,80,1,POST,192.168.88.115,/goform/svLogin,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",37,83,302.0,Redirect,,,,[],,,,['FqFTVkD4Ln1XgU536'],['text/plain'],['FsQCch1HvtBMi0Cdu2'],['text/html']
+2015-10-21 11:09:55.963522911+00:00,C7rp5z4DiciQClYhgl,192.168.2.42,51082,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:10:00.866364002+00:00,CVSVbf1JzhhYUYbE5c,192.168.2.42,51084,192.168.88.115,80,1,GET,192.168.88.115,/ipconfig.asp,http://192.168.88.115/login.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,13992,200.0,OK,,,,[],,,,,,['FwfLbx18Y9ud6PWcxi'],['text/html']
+2015-10-21 11:10:01.074757099+00:00,C4o6KS1RmZpBRv3uLj,192.168.2.42,51085,192.168.88.115,80,1,GET,192.168.88.115,/jscript/ipconfig.js,http://192.168.88.115/ipconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,7665,200.0,OK,,,,[],,,,,,['FLusnh2iVi0XwZ4Qnl'],['text/plain']
+2015-10-21 11:10:01.112154007+00:00,CjhXTdmIKciXzYJe,192.168.2.42,51086,192.168.88.115,80,1,GET,192.168.88.115,/images/block.gif,http://192.168.88.115/ipconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,812,200.0,OK,,,,[],,,,,,['F4QyJf2FpjnKOXlrs8'],['image/gif']
+2015-10-21 11:10:01.328958035+00:00,CCwBhg4e5hjwzKBqW8,192.168.2.42,51087,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/ipconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:10:17.496459961+00:00,COc29u37zCBWKUJtZ5,192.168.2.42,51088,192.168.88.115,80,1,GET,192.168.88.115,/snmpconfig.asp,http://192.168.88.115/ipconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,16018,200.0,OK,,,,[],,,,,,['FOzdas1bWm3ZZwjFWf'],['text/html']
+2015-10-21 11:10:17.621522903+00:00,CVk1Cd374yasNk5X24,192.168.2.42,51089,192.168.88.115,80,1,GET,192.168.88.115,/jscript/snmpconfig.js,http://192.168.88.115/snmpconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1915,200.0,OK,,,,[],,,,,,['FgpKNl3kBbPvPU1SKd'],['text/plain']
+2015-10-21 11:10:17.898592949+00:00,CtnrsC8PW3ttAe6Qd,192.168.2.42,51090,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/snmpconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:10:29.252157927+00:00,CznHlb4OLfq7hIUgK2,192.168.2.42,51091,192.168.88.115,80,1,GET,192.168.88.115,/syslogserverconfig.asp,http://192.168.88.115/snmpconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,12751,200.0,OK,,,,[],,,,,,['FTz3ik3EEEGqpSuuk7'],['text/html']
+2015-10-21 11:10:29.380840063+00:00,C0J8iv42yK6nVZ083a,192.168.2.42,51092,192.168.88.115,80,1,GET,192.168.88.115,/jscript/syslogserverconfig.js,http://192.168.88.115/syslogserverconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1761,200.0,OK,,,,[],,,,,,['FB6pQk3vGSuYndf0Xg'],['text/plain']
+2015-10-21 11:10:29.620543003+00:00,CYYZGg25zrxWbzp6yl,192.168.2.42,51093,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/syslogserverconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:10:36.662204981+00:00,CqvxpKZOeKztkSNTj,192.168.2.42,51094,192.168.88.115,80,1,GET,192.168.88.115,/spconfig.asp,http://192.168.88.115/syslogserverconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,18663,200.0,OK,,,,[],,,,,,['FXUDZV20J8XwOwwe3a'],['text/html']
+2015-10-21 11:10:36.849637032+00:00,CRq3GfvMET1Ohyj7c,192.168.2.42,51095,192.168.88.115,80,1,GET,192.168.88.115,/jscript/spconfig.js,http://192.168.88.115/spconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,3763,200.0,OK,,,,[],,,,,,['FHzSLEKvjsqRr0a87'],['text/plain']
+2015-10-21 11:10:36.850245953+00:00,CIGb6T18I3fMqG6p59,192.168.2.42,51096,192.168.88.115,80,1,GET,192.168.88.115,/jscript/slidemenu.js,http://192.168.88.115/spconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,10307,200.0,OK,,,,[],,,,,,['FpCs9l2PApplt7xTt1'],['text/plain']
+2015-10-21 11:10:37.508888959+00:00,CNfaJp2wnaFXEdS3h,192.168.2.42,51097,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/spconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:10:51.539155960+00:00,CKbev8ZJnYSrDQJDb,192.168.2.42,51098,192.168.88.115,80,1,GET,192.168.88.115,/spconnect.asp,http://192.168.88.115/spconfig.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,16705,200.0,OK,,,,[],,,,,,['FL5Usa3ejsVrIvsNG3'],['text/html']
+2015-10-21 11:10:51.694066048+00:00,CT7cp02mRoS983ddx8,192.168.2.42,51099,192.168.88.115,80,1,GET,192.168.88.115,/jscript/spconnect.js,http://192.168.88.115/spconnect.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,22943,200.0,OK,,,,[],,,,,,['FBpXVw3tt9DeJ0A3yb'],['text/plain']
+2015-10-21 11:10:52.363404036+00:00,CvyZia4HSYzqPtQJP3,192.168.2.42,51100,192.168.88.115,80,1,GET,192.168.88.115,/images/connect.gif,http://192.168.88.115/spconnect.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,105,200.0,OK,,,,[],,,,,,['FrHCpGwjM13dn6q8l'],['image/gif']
+2015-10-21 11:10:52.410212994+00:00,C6YjAD3ZQtXUphT226,192.168.2.42,51101,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/spconnect.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:11:03.128021002+00:00,CGSfTL1QIjWSob5Bs9,192.168.2.42,51102,192.168.88.115,80,1,GET,192.168.88.115,/stserial.asp,http://192.168.88.115/spconnect.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,24638,200.0,OK,,,,[],,,,,,['F9BFY83pkqh1o55K43'],['text/html']
+2015-10-21 11:11:03.264405966+00:00,CvBA6q2GuAeP6oKWp,192.168.2.42,51103,192.168.88.115,80,1,GET,192.168.88.115,/jscript/statistics.js,http://192.168.88.115/stserial.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1556,200.0,OK,,,,[],,,,,,['FuUYlo4SuXraM2KES1'],['text/plain']
+2015-10-21 11:11:03.398932934+00:00,CONJbn4JxQtzsd2GU2,192.168.2.42,51104,192.168.88.115,80,1,GET,192.168.88.115,/images/on.gif,http://192.168.88.115/stserial.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1007,200.0,OK,,,,[],,,,,,['FVyPsa2FLeyIFWO9Sj'],['image/gif']
+2015-10-21 11:11:03.540461063+00:00,Chc0AJ3nTvwoV3sQr6,192.168.2.42,51105,192.168.88.115,80,1,GET,192.168.88.115,/images/off.gif,http://192.168.88.115/stserial.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,946,200.0,OK,,,,[],,,,,,['FEy8Mz4PVGDuwqhUC8'],['image/gif']
+2015-10-21 11:11:03.625031948+00:00,C0O3VW2cXu55y6Yb6f,192.168.2.42,51106,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/stserial.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:11:13.612586021+00:00,CZmkY41H9UoMmPhVs4,192.168.2.42,51107,192.168.88.115,80,1,GET,192.168.88.115,/stserial.asp?refresh_rate=10,http://192.168.88.115/stserial.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,24638,200.0,OK,,,,[],,,,,,['FReZXY2PyhhILJaEOc'],['text/html']
+2015-10-21 11:11:13.739737034+00:00,C4b6444vuLcqFTQe98,192.168.2.42,51108,192.168.88.115,80,1,GET,192.168.88.115,/jscript/statistics.js,http://192.168.88.115/stserial.asp?refresh_rate=10,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1556,200.0,OK,,,,[],,,,,,['FkHTOC2Z9zNUHl9ji8'],['text/plain']
+2015-10-21 11:11:13.876434088+00:00,CBTuqj3YPdLZDJRNCa,192.168.2.42,51109,192.168.88.115,80,1,GET,192.168.88.115,/images/off.gif,http://192.168.88.115/stserial.asp?refresh_rate=10,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,946,200.0,OK,,,,[],,,,,,['FDs5HU1dB57koiIXi8'],['image/gif']
+2015-10-21 11:11:14.041130066+00:00,CnQlcB4neRrk40Sz6l,192.168.2.42,51110,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/stserial.asp?refresh_rate=10,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:11:19.902605057+00:00,CO32Ro3Ab07yzBgnba,192.168.2.42,51111,192.168.88.115,80,1,GET,192.168.88.115,/sysstatus.asp,http://192.168.88.115/stserial.asp?refresh_rate=10,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,13130,200.0,OK,,,,[],,,,,,['FZtzqUu2JWrIvqp9g'],['text/html']
+2015-10-21 11:11:20.051872969+00:00,C806Qv4BTbCHwa22ll,192.168.2.42,51112,192.168.88.115,80,1,GET,192.168.88.115,/jscript/slidemenu.js,http://192.168.88.115/sysstatus.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,10307,200.0,OK,,,,[],,,,,,['FBybX03v0vvpwgJxRj'],['text/plain']
+2015-10-21 11:11:20.054157019+00:00,CJ153ljltzYHpzbzc,192.168.2.42,51113,192.168.88.115,80,1,GET,192.168.88.115,/jscript/sysstatus.js,http://192.168.88.115/sysstatus.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,1271,200.0,OK,,,,[],,,,,,['FfdsRe1f5S0p5s5ky6'],['text/plain']
+2015-10-21 11:11:24.106808901+00:00,CgEDel1KfgK3xHJZlf,192.168.2.42,51115,192.168.88.115,80,1,GET,192.168.88.115,/syslogging.asp,http://192.168.88.115/sysstatus.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,15547,200.0,OK,,,,[],,,,,,['Fw5kqo156rgPJz2OUj'],['text/html']
+2015-10-21 11:11:24.252007961+00:00,C5WlTV3lPbpbwF2zo7,192.168.2.42,51116,192.168.88.115,80,1,GET,192.168.88.115,/jscript/syslogging.js,http://192.168.88.115/syslogging.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,6390,200.0,OK,,,,[],,,,,,['FXtxFL9Kp9lqQCXFi'],['text/plain']
+2015-10-21 11:11:20.661318064+00:00,CH3z3Z1BsMK3LfM5jk,192.168.2.42,51114,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/sysstatus.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:11:24.523350+00:00,C61WBr3I5FtgziCnS9,192.168.2.42,51117,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/syslogging.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
+2015-10-21 11:11:32.203536034+00:00,CILAg644oDOBZcVFr1,192.168.2.42,51118,192.168.88.115,80,1,GET,192.168.88.115,/userloggedonlist.asp,http://192.168.88.115/syslogging.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,11836,200.0,OK,,,,[],,,,,,['FFed7Q2FFo2Xw8LHbb'],['text/html']
+2015-10-21 11:11:32.711894035+00:00,Ce45JD2vXzcXz14dDd,192.168.2.42,51119,192.168.88.115,80,1,GET,192.168.88.115,/favicon.ico,http://192.168.88.115/userloggedonlist.asp,"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36",0,0,,,,,,[],,,,,,,
diff --git a/README.md b/README.md
index 61b2145..7cfbffa 100644
--- a/README.md
+++ b/README.md
@@ -1,2 +1,83 @@
 # Blog Code
-This repo hosts examples used throughout my blog posts at [dgunter.com](http://www.dgunter.com).
+
+Jupyter notebooks from the *Threat Hunting with Python* series I wrote in 2017
+and 2018, brought up to date so they still run. Each notebook works through a
+hunt on a real dataset that ships with the repo: a day of Bro HTTP logs from
+the 2015 4SICS conference lab network, and a packet capture of Microsoft SQL
+Server traffic between a historian and its clients.
+
+The posts themselves lived on dgunter.com, which is offline, and were
+syndicated on the Dragos blog. They are preserved under [docs/](docs/) with
+their images so the code has its narrative next to it.
+
+## The notebooks
+
+| Notebook | The hunt | Companion post |
+| --- | --- | --- |
+| [Bro HTTP Nmap Indicator Detection](Bro%20HTTP%20Log%20Analysis/Bro%20HTTP%20Nmap%20Indicator%20Detection.ipynb) | Stack user agents and URIs, search for the Nmap Scripting Engine, and export every request between scanner and target as CSV | [Prologue and Basic HTTP Hunting](docs/2017-09-threat-hunting-with-python-prologue.md) (2017-09) |
+| [Detecting Nmap Behavior with Bro HTTP Logs](Bro%20HTTP%20Log%20Analysis/Detecting%20Nmap%20Behavior%20with%20Bro%20HTTP%20Logs.ipynb) | The same log as a time series: user agents and status codes per minute, with the scan window standing out once the busy browser is removed | [Part 2: Detecting Nmap Behavior with Bro HTTP Logs](docs/2017-11-threat-hunting-with-python-part-2-nmap-bro-http.md) (2017-11) |
+| [Detecting Nmap Behavior with ParseZeekLogs](Bro%20HTTP%20Log%20Analysis/Detecting%20Nmap%20Behavior%20with%20ParseZeekLogs.ipynb) | Both hunts above redone with [ParseZeekLogs](https://github.com/dgunter/ParseZeekLogs) and pandas, added in 2026 | same two posts |
+| [tds](TDS%20Analysis/tds.ipynb) | Pull TDS packet types, SQL batches and remote procedure calls out of a pcap with pyshark and look at what a historian's clients actually run | [Part 4: Examining Microsoft SQL Based Historian Traffic](docs/2018-03-threat-hunting-with-python-part-4-mssql-historian.md) (2018-03) |
+
+[Part 3: Taming SMB](docs/2018-02-threat-hunting-with-python-part-3-taming-smb.md)
+(2018-02) is preserved too; its examples were shown inline rather than as a
+notebook.
+
+The TDS notebook works from the packet capture rather than from Zeek logs
+because Zeek has no TDS analyzer, then or now: Zeek 8.2.2 run over the same
+pcap produces only `conn.log` for the port 1433 sessions. pyshark hands the
+fields Wireshark's TDS dissector decodes to pandas instead.
+
+The 2017 notebooks split each log line on tabs and count values in
+dictionaries. They were written for Python 2.7 and have been ported to run on
+Python 3 with current pandas and matplotlib, otherwise as written, so the
+output you see is what the posts describe. The ParseZeekLogs notebook is the
+version I would write today: the log is read with typed fields by a library
+that understands the Zeek format, and `groupby` does the counting. It runs
+unchanged on logs from current Zeek releases (checked against Zeek 8.2.2 TSV
+and JSON output); the bundled log is the original 2015 one so the results match
+the posts.
+
+## Running them
+
+Requires Python 3.10 or newer and [uv](https://docs.astral.sh/uv/). The TDS
+notebook also needs `tshark` from Wireshark on your `PATH`
+(`brew install wireshark` on macOS, `apt install tshark` on Debian and Ubuntu).
+
+```bash
+uv sync
+uv run jupyter lab
+```
+
+Open a notebook from the folder it lives in; each reads its data file by a
+relative path. To execute everything from the command line, the way CI does:
+
+```bash
+uv run jupyter nbconvert --to notebook --execute --inplace "Bro HTTP Log Analysis/"*.ipynb "TDS Analysis/"*.ipynb
+```
+
+`uv run ruff check .` lints the code cells. The GitHub Actions workflow runs
+the lint and executes every notebook on each push and pull request, and fails
+if any cell errors or writes to stderr.
+
+## Data
+
+- `Bro HTTP Log Analysis/http.log`: 5,758 HTTP records from one day of the
+  4SICS 2015 lab network, in Bro TSV format.
+- `Bro HTTP Log Analysis/suspicious_http_records.csv` and
+  `suspicious_http_records_parsezeeklogs.csv`: the 299 requests between the
+  scanning clients and their targets, as written by the two notebooks.
+- `TDS Analysis/ms-sql-tds-rpc-requests.pcap`: 30 TDS packets between a
+  Microsoft SQL Server historian and its clients.
+
+## Related
+
+- [ParseZeekLogs](https://github.com/dgunter/ParseZeekLogs): read Zeek logs
+  as typed records, or load them into Elasticsearch in Elastic Common Schema.
+- [evtxtoelk](https://github.com/dgunter/evtxtoelk): the same idea for
+  Windows Event Logs.
+
+## License
+
+MIT, see [LICENSE](LICENSE). The posts under `docs/` are my own writing,
+reproduced from the Wayback Machine.
diff --git a/TDS Analysis/tds.ipynb b/TDS Analysis/tds.ipynb
index 5e48d49..f8f941b 100644
--- a/TDS Analysis/tds.ipynb	
+++ b/TDS Analysis/tds.ipynb	
@@ -4,30 +4,80 @@
    "cell_type": "code",
    "execution_count": 1,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:56.880917Z",
+     "iopub.status.busy": "2026-09-05T22:08:56.880766Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.045490Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.044971Z"
+    }
    },
    "outputs": [],
    "source": [
-    "import pyshark\n",
-    "import pandas as pd"
+    "import asyncio\n",
+    "from concurrent.futures import ThreadPoolExecutor\n",
+    "\n",
+    "import pandas as pd\n",
+    "import pyshark"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.046702Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.046615Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.455048Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.454609Z"
+    }
    },
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "30 TDS packets\n"
+     ]
+    }
+   ],
    "source": [
     "# Read packet capture and set display filter to only retrieve TDS traffic\n",
-    "cap = pyshark.FileCapture(\"ms-sql-tds-rpc-requests.pcap\",display_filter=\"tds\")"
+    "# pyshark drives tshark through asyncio and needs an event loop of its own. Jupyter's kernel\n",
+    "# already runs one in the main thread, so read the capture in a worker thread with a fresh loop.\n",
+    "# Python 3.12+ also no longer creates a loop implicitly, so this is needed outside Jupyter too.\n",
+    "\n",
+    "\n",
+    "def read_capture(path, display_filter):\n",
+    "    def work():\n",
+    "        cap = pyshark.FileCapture(\n",
+    "            path, display_filter=display_filter, eventloop=asyncio.new_event_loop()\n",
+    "        )\n",
+    "        try:\n",
+    "            return list(cap)\n",
+    "        finally:\n",
+    "            cap.close()\n",
+    "\n",
+    "    with ThreadPoolExecutor(max_workers=1) as pool:\n",
+    "        return pool.submit(work).result()\n",
+    "\n",
+    "\n",
+    "cap = read_capture(\"ms-sql-tds-rpc-requests.pcap\", display_filter=\"tds\")\n",
+    "print(len(cap), \"TDS packets\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 3,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.456112Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.456040Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.460465Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.460125Z"
+    }
+   },
    "outputs": [],
    "source": [
     "tds_types = {\n",
@@ -91,12 +141,12 @@
     "                    pkt_data[\"rpc_proc_id\"] = procedure_codes.get(pkt.tds.rpc_proc_id)\n",
     "                else:\n",
     "                    pkt_data[\"rpc_proc_id\"] = int(pkt.tds.rpc_proc_id)\n",
-    "            except:\n",
+    "            except AttributeError:\n",
     "                pass\n",
     "            # Look for a procedure name\n",
     "            try:\n",
     "                pkt_data[\"rpc_proc_name\"] = str(pkt.tds.rpc_name)\n",
-    "            except:\n",
+    "            except AttributeError:\n",
     "                pass\n",
     "        else:\n",
     "            # Not a RPC or query\n",
@@ -109,24 +159,31 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.461372Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.461312Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.468313Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.467878Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -154,7 +211,7 @@
        "      <td>1433</td>\n",
        "      <td>1</td>\n",
        "      <td>SQL Batch</td>\n",
-       "      <td>set transaction isolation level  read committ...</td>\n",
+       "      <td>set transaction isolation level&nbsp;&nbsp;read committ...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "    </tr>\n",
@@ -258,7 +315,7 @@
        "      <td>1433</td>\n",
        "      <td>1</td>\n",
        "      <td>SQL Batch</td>\n",
-       "      <td>set transaction isolation level  read committ...</td>\n",
+       "      <td>set transaction isolation level&nbsp;&nbsp;read committ...</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
        "    </tr>\n",
@@ -455,7 +512,7 @@
        "      <td>RPC</td>\n",
        "      <td></td>\n",
        "      <td></td>\n",
-       "      <td>p_SaveExample</td>\n",
+       "      <td></td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>24</th>\n",
@@ -660,7 +717,7 @@
        "20                                        \n",
        "21                                        \n",
        "22                                        \n",
-       "23                         p_SaveExample  \n",
+       "23                                        \n",
        "24                      p_SetBogusSample  \n",
        "25           p_GetMyExampleTableRowCount  \n",
        "26  proc_GetMyExampleTableSampleMetaData  \n",
@@ -684,15 +741,23 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.469105Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.469055Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.471213Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.470919Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "query\n",
        "                                                                                    27\n",
        " set transaction isolation level  read committed  set implicit_transactions off      2\n",
        "COMMIT TRANSACTION                                                                   1\n",
-       "Name: query, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
      "execution_count": 5,
@@ -708,21 +773,28 @@
   {
    "cell_type": "code",
    "execution_count": 6,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.472061Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.472011Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.474055Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.473754Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "                                        21\n",
+       "rpc_proc_name\n",
+       "                                        22\n",
        "proc_GetMyExampleTableSampleMetaData     2\n",
        "p_GetBogusData                           1\n",
-       "p_GetMyExampleTableRowCount              1\n",
        "sp_executesql                            1\n",
-       "proc_FetchMyExampleData                  1\n",
-       "p_SaveExample                            1\n",
        "p_SetBogusSample                         1\n",
+       "p_GetMyExampleTableRowCount              1\n",
+       "proc_FetchMyExampleData                  1\n",
        "dbo.proc_GetMySampleDataItems            1\n",
-       "Name: rpc_proc_name, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
      "execution_count": 6,
@@ -738,15 +810,23 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.474990Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.474930Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.477229Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.476860Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/plain": [
+       "rpc_proc_id\n",
        "               23\n",
        "Sp_PrepExec     5\n",
        "Sp_Execute      2\n",
-       "Name: rpc_proc_id, dtype: int64"
+       "Name: count, dtype: int64"
       ]
      },
      "execution_count": 7,
@@ -762,24 +842,31 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.478100Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.478051Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.480865Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.480523Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -820,24 +907,31 @@
   {
    "cell_type": "code",
    "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-09-05T22:08:57.481769Z",
+     "iopub.status.busy": "2026-09-05T22:08:57.481714Z",
+     "iopub.status.idle": "2026-09-05T22:08:57.484544Z",
+     "shell.execute_reply": "2026-09-05T22:08:57.484207Z"
+    }
+   },
    "outputs": [
     {
      "data": {
       "text/html": [
        "<div>\n",
-       "<style>\n",
-       "    .dataframe thead tr:only-child th {\n",
-       "        text-align: right;\n",
-       "    }\n",
-       "\n",
-       "    .dataframe thead th {\n",
-       "        text-align: left;\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
        "    }\n",
        "\n",
        "    .dataframe tbody tr th {\n",
        "        vertical-align: top;\n",
        "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
        "</style>\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
@@ -887,21 +981,21 @@
  ],
  "metadata": {
   "kernelspec": {
-   "display_name": "Python 2",
+   "display_name": "Python 3",
    "language": "python",
-   "name": "python2"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
     "name": "ipython",
-    "version": 2
+    "version": 3
    },
    "file_extension": ".py",
    "mimetype": "text/x-python",
    "name": "python",
    "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython2",
-   "version": "2.7.14"
+   "pygments_lexer": "ipython3",
+   "version": "3.14.7"
   }
  },
  "nbformat": 4,
diff --git a/docs/2017-09-threat-hunting-with-python-prologue.md b/docs/2017-09-threat-hunting-with-python-prologue.md
new file mode 100644
index 0000000..02e5bf2
--- /dev/null
+++ b/docs/2017-09-threat-hunting-with-python-prologue.md
@@ -0,0 +1,57 @@
+# Threat Hunting with Python: Prologue and Basic HTTP Hunting
+
+*Originally published at [dgunter.com](https://dgunter.com/2017/09/17/threat-hunting-with-python-prologue-and-basic-http-hunting/) on 2017-09-17; recovered from the [Wayback Machine](http://web.archive.org/web/20180221151955/https://dgunter.com/2017/09/17/threat-hunting-with-python-prologue-and-basic-http-hunting/) on 2026-09-05.*
+
+## **Prologue: Begin the Hunt**
+
+You’ve developed a solid network and host-based detection strategy. You are comfortable with the technology you purchased, and processes you developed should you do start getting alerts and might need to open an investigation. You might wonder what the next step should be. The truth is no product or process is perfect nor truly comprehensive at detecting an intrusion into your network. There is no silver bullet in security. Just as defenders adapt and overcome defenses to meet weaknesses, attackers also adapt and overcome to bypass new layers of defense. The creation of a reliable threat hunting strategy and implementation of that strategy can help overcome gaps present in your detection plan. Threat hunting is a human-driven defensive process that seeks to uncover entrenched threats beyond the capabilities of existing protective layers.
+
+The hunting process consists of developing a hypothesis for where an attacker might be found, collecting the necessary artifacts to prove or disprove the generated hypothesis, analyzing the collected evidence, and finally automating the steps taken to save time in the future. The purpose of this series is to help with the automation piece. Over the next few weeks, we will look at basic analytic approaches that can be taken to examine some of the most common protocols found on typical networks. This week we will get started with basic HTTP analysis using Python and Jupyter notebooks. Python is a powerful and easy to use scripting language with a relatively light learning curve relative to other options. If you are just here for strategy and not the coding portion, no need to set up your python environment. If you are following along, we created a GitHub repo with the code [here](https://github.com/dgunter/Blog-Code/tree/master/Bro%20HTTP%20Log%20Analysis).
+
+Jupyter Notebooks can be installed using the instructions on [Jupyter’s webpage](https://jupyter.org/install.html). You can install Jupyter via pip if you want, but I recommend following the advice about using Anaconda. The community edition of the [Anaconda Platform](https://www.anaconda.com/download/) includes both Python and Jupyter Notebooks as well as several other data science tools and works across Windows, OS X and Linux. Best of all, Anaconda’s community edition is completely free. We used Python 2.7, however, the code we will be writing works on either. Download and install Anaconda before you proceed to the next portion if you plan to follow along with the source code. Once you have Anaconda installed, you should be able to find Jupyter notebooks on the Windows start menu or be able to launch the anaconda-Navigator on OS X and then launch Jupyter notebooks.
+
+## **Part 1: Analyzing HTTP**
+
+Hypertext Transfer Protocol (HTTP) is a staple protocol both for traditional endpoint systems and many embedded devices. Everything from vehicle entertainment systems to [Ronald Teune’s modified Philips Senseo coffee machine](http://www.ronaldteune.nl/projects/index.php/Coffee_machine) to the servers and clients that make up the corporate networks that enable businesses to be productive use HTTP to produce and consume data. As networks grow larger, so does the volume of HTTP traffic threat hunters must comb through. Automating analysis will be the only way to keep up with the amount of traffic.
+
+Today, we will be using logs from the [Bro Intrusion Detection System](https://www.bro.org/). Bro originated from a team working at the International Computer Science Institute in Berkeley, CA, and the National Center for Supercomputing Applications in Urbana-Champaign, IL. Bro provides a free and open source platform that can be scaled to handle large volumes of traffic and, most importantly to us, logs that suit threat hunting efforts well. You can mirror live traffic off your network into a Bro instance or feed a packet capture file through Bro to generate the needed logs for hunting. The HTTP log we will examine came from a single day of the 2015 4SICS security conference’s lab network which is an industrial control system security conference now named CS3STHLM. While it would be possible to analyze this file manually, scripting saves a lot of time and prevents human error.
+
+The first threat hunting approach we will use is called stacking. Stacking is a simple but useful way to look at outliers in a set of data by counting encountered values within a log. The first value we will stack on is the user agent. User agents can easily be modified, but that doesn’t mean an attacker will remember or not make a mistake with a scanning tool. Let’s develop a quick hypothesis about user agents and start hunting.
+
+**Hypothesis 1:** *Attackers will use scanning and service enumeration tools during reconnaissance with identifiable characteristics that show up in the user agent and URI field of HTTP logs.*
+
+Now that we have a testable hypothesis and we have collected the log files needed to prove or disprove the developed hypothesis, we move into the analysis phase. Here are the results of stacking user agents:
+
+![img0](images/prologue-1.png)
+
+Only seven unique user agents were seen in the HTTP logs. One of the user agents that immediately stands out hopefully is the second one that contains Nmap Scripting Engine. The number after the colon indicates this user agent was seen 171 times. Let’s now use another hunting technique known as searching to retrieve all records that contain the Nmap user agent.  We are going to search for the user agent and format our data results as a JSON document containing the client who made the request as the outer key, the servers who handled requests from a given client as the inner key and finally the number of requests as the inner value to the inner key. If this doesn’t make sense, check the output and commentary below.
+
+![img1](images/prologue-2.png)
+
+We now see the two clients that used Nmap, the hosts they made requests to on the network, and the number of requests made. This list only contains requests that use the particular user agent. Now we will go back and look at all requests between the clients and servers above and look at other HTTP activity around the time the scans took place.
+
+![img2](images/prologue-3.png)
+
+The resulting CSV file contains all the information from the Bro HTTP logs. After a quick scan of the CSV file, you should be able to see that HTTP interaction from 192.168.2.64 is only limited to Nmap scans. 192.168.2.42 scans a single machine and then begins to interact with the website on port 80. We’ve now proven our hypothesis that HTTP reconnaissance was used within the traffic capture in question.
+
+## **Takeaways**
+
+The findings so far should feed into the rest of the investigation cycle to complete the rest of your organization’s event response plan. The event response plan should contain steps to validate that the scan was or wasn’t authorized on the subnet in question. HTTP logs are one potentially useful resource, but the investigation shouldn’t stop after examining just one log type.
+
+- We started with a suspicious user agent and ended up finding to machines performing Nmap scans on the network.
+- We discovered useful HTTP based Nmap scan indicators for later hunts
+- Automation should account for attacker modification of a subset of fields as attackers might modify indicators before scanning
+
+### Potential Nmap Indicators in HTTP Logs
+
+### img3.png
+
+### **What’s Next**
+
+We produced indicators this week to detect nmap scans based on HTTP traffic. While indicators are useful for detection of known threats, behavioral detection can move beyond just identifying known threats and can potentially detect unknown scanners or scanners that you don’t already have indicators for. Check back in two weeks, and we will explore a behavioral approach.
+
+All code related to the examples above are in [this Github repo](https://github.com/dgunter/Blog-Code/tree/master/Bro%20HTTP%20Log%20Analysis).
+
+If you have any questions or topic suggestions, feel free to reach out to me on [twitter](https://twitter.com/dan_gunter) or [LinkedIn](https://www.linkedin.com/in/dan-gunter).
+
+Title image from [Nina Matthews Photography](https://www.flickr.com/photos/21560098@N06/) used under the Creative Commons License.
diff --git a/docs/2017-11-threat-hunting-with-python-part-2-nmap-bro-http.md b/docs/2017-11-threat-hunting-with-python-part-2-nmap-bro-http.md
new file mode 100644
index 0000000..9878afc
--- /dev/null
+++ b/docs/2017-11-threat-hunting-with-python-part-2-nmap-bro-http.md
@@ -0,0 +1,67 @@
+# Threat Hunting with Python Part 2: Detecting Nmap Behavior with Bro HTTP Logs
+
+*Originally published at [dgunter.com](https://dgunter.com/2017/11/28/threat-hunting-with-python-part-2-detecting-nmap-behavior-with-bro-http-logs/) on 2017-11-28; recovered from the [Wayback Machine](http://web.archive.org/web/20190711004650/https://dgunter.com/2017/11/28/threat-hunting-with-python-part-2-detecting-nmap-behavior-with-bro-http-logs/) on 2026-09-05.*
+
+This is the second part of a series I originally **[posted on the Dragos Blog](https://dragos.com/blog/20171121ThreatHuntingWithPythonPt2.html).**
+
+## Prologue: Begin the Hunt
+
+In the last edition of this series, we detected Nmap scans by looking for URI indicators associated with Nmap. This week we are going to move away from URI based indicators and focus on Nmap’s behavior on the network. The notebook used for analysis can be found [here on GitHub](https://github.com/dgunter/Blog-Code/blob/master/Bro%20HTTP%20Log%20Analysis/Detecting%20Nmap%20Behavior%20with%20Bro%20HTTP%20Logs.ipynb).
+
+Last time we analyzed URIs found in HTTP logs to find Nmap scans. This technique worked because certain Nmap modules use hardcoded URIs. We saw the request for flumemaster.jsp due to the [flume-master-info nmap script](https://github.com/nmap/nmap/blob/master/scripts/flume-master-info.nse) that attempts to retrieve information from Apache Flume servers. Dfshealth.jsp comes from Nmap’s [Hadoop-NameNode-info nmap script](https://github.com/nmap/nmap/blob/master/scripts/hadoop-namenode-info.nse) to retrieve data from Apache Hadoop’s NameNode host. The request for “/nice ports,/Trinity.txt.bak” comes from Nmap’s service detection routine testing how a server [handles escape characters within a URI](http://seclists.org/nmap-dev/2006/q2/162). The actual request is “GET /nice%20ports%2C/Tri%6Eity.txt%2ebak HTTP/1.0\r\n\r\n”.
+
+But what happens when an attacker doesn’t run all the modules that generate the requests to the above URIs? The individual(s) responsible for the scans that generated this log output likely ran Nmap with the ‘–a’ option that caused all Nmap scripts on the machine to run. A more sophisticated attacker will probably be more conservative with the footprint left on the victim network. This week we will move away from hard-coded indicators and begin to look at behavioral indicators. Behavioral indicators allow identification of scanning in an environment beyond just that of Nmap.
+
+## Dataset Recap
+
+The dataset used for analysis again comes from the 2015 4SICS conference geek lounge which featured both traditional endpoint systems and Industrial Control System devices. A full packet capture and the corresponding Bro IDS logs are available on automayt’s [GitHub repo](https://github.com/automayt/ICS-pcap/tree/master/Additional%20Captures/4SICS-GeekLounge-151021). The http.log file found in the repo comes from Bro IDS’ analysis of the packet capture.
+
+##### User Agent Analysis
+
+Let’s begin by looking at user agents within the environment. For organizations with mature software inventory management policies, user agents tend to be static. Almost all traffic tends to originate from the top two or three user agents on the network as these are the approved applications. The same is true within the production and OT portion of industrial networks where the environment is relatively static from a change perspective. Let’s first map the user agents seen in the environment over time. Since the pcap we have only covered five hours, we are going to break down our analysis into minute longs groups. The below image is only a subset of the entire user agent table.
+
+![Subset of user agents](images/part2-1.png)
+
+Within the sample data, “Mozilla/5.0 (X11; Linux x86\_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/46.0.2490.64 Safari/537.36” is the clear winner for the most used user agent. This sample data isn’t representative of a typical corporate or industrial network as it came from a security conference but does demonstrate the typical distribution of a static network. With a single line of code, we can generate a plot from this table.
+
+This graph isn’t handy because of the frequency of the top user agent. Let’s massage the data a bit to see if we can get a more useful view. We are going to remove the most frequent user agent and re-generate the table and plot. The most frequent user agent doesn’t look immediately suspicious, but should still be checked against threat intelligence and other sources to test for association with other intrusions.
+
+![Graph with most frequent user agent removed](images/part2-2.png)
+
+Hopefully, the user agent named “Mozilla/5.0 (compatible; Nmap Scripting Engine; <http://nmap.org/book/nse.html>)” immediately stands out to you. This user agent is another good quick indicator of Nmap scans but isn’t behavioral. The other interesting user agent is “-” which means no user agent was present (Bro IDS uses “-” in logs to indicate no value present).
+
+Using this graph, we also quickly see Nmap ran from 0500-0700 on 2015-10-21. The no user agent activity, however, runs from 0500-0800. User agents aren’t an excellent source alone to find malicious behavior as developers who code applications that make HTTP requests can forget to add user agents to the request code. In the same manner, malware developers can set user agents to just about anything. In practice, we’ve noticed an increase in no user agents observed on the network during Nmap scans. Context is vital in helping security analysts understand if and when a given user agent should occur.
+
+## Status Code Analysis
+
+User agents provide one possible indicator to hunt off, but are client side and can easily be manipulated by attackers. Another characteristic of HTTP sessions optimal hunting are status codes. Status code definitions come from the HTTP specification document and break into five categories. 1xx series codes are informational, 2xx series codes indicate success, 3xx codes indicate the client must redirect to obtain the requested information, 4xx series codes indicate client errors and 5xx series codes represent server errors. The most common status codes you will likely encounter are 200 indicating an HTTP request was successful, 301 indicating the requested resource has moved and 404 indicating the server could not find what the client requested.
+
+In our earlier discussion, we looked at several URIs present in Nmap modules. Flumemaster.jsp and “/nice ports,/Trinity.txt.bak” are two examples from earlier. When Nmap requests this URI, the server will likely respond with a 404 status code unless these files exist on the server. Other Nmap modules result in other HTTP status codes being returned based on the request made. Our analysis will focus on the “normal” distribution of HTTP status codes on a given network. Status code analysis allows us to look for outliers that might represent malicious traffic. Industrial networks are a prime candidate for HTTP status code analysis as the number of unique status codes seen is typically very limited.
+
+Let’s get startyed by looking at the distribution of HTTP status codes.
+
+![Pie chart with status code distribution.  Most are 200s](images/part2-3.png)
+
+As expected, the 200 status code for success dominated the results. We also see quite a few 3xx redirect status codes, a mix of 4xx client error status codes and a single server error status code. The status code with “-” means no status code was set. Not having a status code should also raise suspicion. Let’s now look deeper and map the status code distribution over time.
+
+![Status code distribution over time.  Initially it is all 200s, but that tapers down abruptly](images/part2-4.png)
+
+The above graph shows the status code quantity over time. As we can see, a majority of the successful HTTP requests occurred very early in the packet capture. This plot is, unfortunately, the only real fact we can derive is that the quantity of 200 status codes dominates the dataset. Let’s remove the 200 status code and redraw the graph.
+
+![Status code distribution over time without 200s included.](images/part2-5.png)
+
+This plot is much more useful. We see a significant spike in at least five types of HTTP status codes in roughly the same spot of the packet capture as our earlier 200 status code spike. We’ve found this status code quantity spike to be very indicative of Nmap scans. Additionally, the presence of the “-” code that indicates no status code present also typically occurs when an attacker uses the “-A” flag with Nmap.
+
+## HTTP in Industrial Environments
+
+Asset owners should focus on the use of HTTP within OT environments. HTTP traffic typically transits between human-machine interfaces (HMI) and users. Certain models of programmable logic controllers (PLC) also contain embedded web servers that use HTTP. Within the OT environment, HTTP usage is somewhat predictable from a behavioral standpoint. Asset owners can significantly benefit from an understanding of the behaviors of devices within their OT environments.
+
+The first step asset owners should take involves the creation of an inventory the characteristics of HTTP clients and hosts within the environment. After collection of the inventory, the indicators associated with each client and server should be identified. Behaviors for each client and server concerning status codes and access behavior are two areas that might be considered. Frequent threat hunts that map the behavior of HTTP then can be used to detect intrusions. The techniques outlined in this post provide a few ideas for a successful hunt.
+
+## Moving Forward
+
+Nmap is not the only network scanner where certain scans can be detected in HTTP traffic. Other scanners could use atypical user agents in your environment not typically present. HTTP status codes provide a more promising option for detecting scanning as any tool that uses a hard-coded URI not present on the scanned server will generate a 404 or other HTTP error message.
+
+Detection of scanning on your network also shouldn’t be done solely with HTTP logs. HTTP logs do however provide a viewpoint many don’t consider when attempting to detect adversary reconnaissance. You won’t detect an attacker that doesn’t use Nmap’s ‘-A’ option or run scripts that generate HTTP traffic as their behavior won’t be exposed in HTTP logs. To detect the attacker mindful of their footprints on the network, you will likely have to use several log types. This doesn’t mean however that HTTP logs should be discounted during threat hunting and detection activities.
+
+In the next edition of this series, we will transition over to Bro communication logs and look at different approaches and scenarios where communication logs are useful during a threat hunt. Have any comments, questions or ideas? Reach out to me on [Twitter](https://twitter.com/dan_gunter), [LinkedIn](https://www.linkedin.com/in/dan-gunter/) or via email at [dgunter@dragos.com](mailto:dgunter@dragos.com).
diff --git a/docs/2018-02-threat-hunting-with-python-part-3-taming-smb.md b/docs/2018-02-threat-hunting-with-python-part-3-taming-smb.md
new file mode 100644
index 0000000..c4d721a
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+++ b/docs/2018-02-threat-hunting-with-python-part-3-taming-smb.md
@@ -0,0 +1,59 @@
+# Threat Hunting with Python and Bro IDS Part 3: Taming SMB
+
+*Originally published at [dgunter.com](https://dgunter.com/2018/02/17/threat-hunting-with-python-and-bro-ids-part-3-taming-smb/) on 2018-02-17; recovered from the [Wayback Machine](http://web.archive.org/web/20190711050412/https://dgunter.com/2018/02/17/threat-hunting-with-python-and-bro-ids-part-3-taming-smb/) on 2026-09-05.*
+
+This is the third part of a series I originally **[posted on the Dragos Blog](https://dragos.com/blog/20180130ThreatHuntingWithPythonPt3.html).**
+
+2017 was a busy year for attackers. Between the WannaCry/Petya/Bad Rabbit ransomware, GOP hacks, Game of Thrones/HBO hacks, Equifax breach and the TRISIS malware that targeted industrial control safety systems in the Middle East, many new proven attack scenarios developed that defenders should account for within their security strategies. This week we will focus on the Server Message Block (SMB) protocol that enabled Wannacry, Petya, and Bad Rabbit attacks to be lethal at the global level and what defenders can do to hunt within this protocol.
+
+## Setting the Stage
+
+On April 14th, 2017 the ShadowBrokers leaked an Equation Group tool called ETERNALBLUE that enabled attackers to exploit Windows machines running SMBv1. Less than a month later ETERNALBLUE allowed the WannaCry ransomware to quickly spread both to systems with SMB exposed to the internet and through a suspected spear phishing campaign. Two months later, ETERNALBLUE acted as a propagation vector during the Petya ransomware campaign. While Microsoft did [release a security bulletin that patched the vulnerabilities a month before the ShadowBroker leak](https://docs.microsoft.com/en-us/security-updates/securitybulletins/2017/ms17-010), the WannaCry and Petya attacks still demonstrated the effectiveness of SMB targeted worms.
+
+## What SMB is and Why it Matters to Industrial Networks
+
+SMB (also commonly known as Common Internet File System or CIFS) is a file and resource sharing protocol that uses TCP and UDP over IP. Development credit for the Server Message Block (SMB) protocol points to an IBM engineer named Dr Barry Feigenbaum who developed the initial protocol specification in the early 1980s. Over the years, Microsoft added additional features and optimizations and eventually released a proprietary version of SMBv2 in 2006 and SMBv3 in 2012. As of today, SMBv2 and SMBv3 provide significant network optimizations to network systems. Microsoft offers a full list of the benefits of SMB [on this page](https://support.microsoft.com/en-us/help/2696547/how-to-detect-enable-and-disable-smbv1-smbv2-and-smbv3-in-windows-and).
+
+Specific to industrial control networks, SMB provides remote directory services that many Windows-based control systems rely on to properly operate. One example includes Honeywell’s Experion Process Knowledge System (EPKS). [Honeywell’s network and security planning guide](https://www.honeywellprocess.com/library/support/Public/Documents/Network_and_Security_Planning_Guide_EXDOC-XX75-en-110.pdf) does an outstanding job of outlining SMB usage within the control network and the file shares involved. Honeywell is just one vendor who uses SMB, but we chose them for this discussion due to the high level of information security relevant information provided.
+
+![Network Security](images/part3-1.png)
+
+As seen in the image above, for a file share server in the process control network (PCN) to provide information to a file share client in the PCN DMZ, the firewall must have the rule to allow SMB traffic between the PCN DMZ and PCN. The business use for this rule might include monitoring alarm data from Experion servers within the PCN. This firewall rule enables machines in the business network to potentially access the file share client in the DMZ but also increases risk as the PCN’s SMB service is now exposed to potential exploitation. In this case, it is recommended to block SMB between the business network and PCN to prevent Wannacry/Petya situations where propagating malware is using SMB.
+
+![Network Security](images/part3-2.png)
+
+The Experion PKS system also defines a specific configuration of access groups and permission levels. Also, Honeywell provides the shares created, location, nodes and general usage of the shares. This type of information is useful for defenders as the data provides valuable context to behavior-based hunting within the environment.
+
+![Shares created and permissions created by Experion LX Tools install](images/part3-3.png)
+
+Specific to account levels, hunters on Honeywell Experion networks now know that accounts in Local Servers and Product Engineers should closely be analyzed as they have full control over the shares. The local supervisors, local operators, local ack view only user and local view only users groups should just have read permissions and pose less risk in an account compromise situation.
+
+We only looked at one of the SMB shares created tables. However, Honeywell provides context on what network shares exist and the location, which system roles should access the network share and what that network share supports. This data provides the background needed to perform system based behavioral threat hunting due to knowing expected behavior.
+
+The information Honeywell provides is all good news for Honeywell based networks, but what if you aren’t using Honeywell? You should use the techniques outlined later in this blog entry to generate information about how SMB works within your environment. Your particular vendor might also provide a guide similar to Honeywell’s network and security planning guide.
+
+Regardless of if the vendor doesn’t have a similar guide, threat hunting hypothesis should be developed and tested to gain a better understanding of your particular installation.
+
+## Let’s go Hunting!
+
+We’ve talked about SMB and SMB’s role in industrial networks. Lets now move onto the practical part and start digging through data. To generate data, we set up two domain controllers. One domain controller simulated the corporate domain while the other simulated a PCN subdomain. The corporate domain sits in the 192.168.5.0/24 IP range while the PCN subdomain sits in the 192.168.6.0/24 subnet.
+
+We used Metasploit’s PSExec exploit tool which is based off Mark Russinovich’s Sysinternals PSExec tool to access machines via SMB remotely. Rapid7 outlines the different PSExec options available in Metasploit in [a blog post here](https://blog.rapid7.com/2013/03/09/psexec-demystified/).
+
+## Auditing User Access
+
+![Auditing User Access](images/part3-4.png)
+
+We used Bro IDS’ SMB module to generate the NT Lan Manager (NTLM) Security Support Provider log file seen above. The traffic we captured only covered 49 total minutes of traffic on a relatively quiet network. As seen above, we can quickly see the source host, destination host, username, hostname, domain name and status for each authentication request. On a production network with a high reliance on SMB, this log will likely be much longer. Python is an ideal tool to analyze more massive log files.
+
+![Auditing User Access](images/part3-5.png)
+
+With a large dataset, a truncated version of the pandas data frame would be output. In our example, we now see the 12 records from the ntlm.log file. With the Bro log file loaded in pandas, we can now use Python to search for specific fields and values we are interested. Within the context of our simulated data, we want to look for potential pivots between the corporate subnet (192.168.5.0/24) and PCN subnet (192.168.6.0/24) or malicious use of privileged or special accounts. Let’s look at the use of the network administrator account going within the PCN subnet.
+
+![Auditing User Access](images/part3-6.png)
+
+As shown above, we have two instances where the administrator account logged into a host within the PCN network. Python is powerful but additional context to the situation on the network traffic adds another very important layer to this analysis. In our mock organization, an administrator account should never authenticate between the corporate and PCN network. Additionally, the context of the source host was an individual engineer’s workstation on the corporate network connecting to the engineer’s workstation within the PCN network and the domain controller of the PCN network. While an extreme scenario, we were able to diagnose and pare down a log file to locate information needed for our investigation.
+
+## Moving Forward
+
+We showed one example of how an organization might investigate SMB using Bro logs and Python. SMB has many more features to explore and opportunities to develop additional analytics. Other areas to study specific to industrial networks should include who is accessing network shares and how, when and how network devices and users leverage SMB services and trust relationships between subnets. The reliance of industrial control system devices on SMB means SMB won’t be going away anytime soon, so organizations should ensure they develop both strategy and processes to manage and secure SMB on both industrial and IT networks.
diff --git a/docs/2018-03-threat-hunting-with-python-part-4-mssql-historian.md b/docs/2018-03-threat-hunting-with-python-part-4-mssql-historian.md
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@@ -0,0 +1,69 @@
+# Threat Hunting With Python Part 4: Examining Microsoft SQL Based Historian Traffic
+
+*Originally published at [dgunter.com](https://dgunter.com/2018/03/20/threat-hunting-with-python-part-4-examining-microsoft-sql-based-historian-traffic/) on 2018-03-20; recovered from the [Wayback Machine](http://web.archive.org/web/20190711052703/https://dgunter.com/2018/03/20/threat-hunting-with-python-part-4-examining-microsoft-sql-based-historian-traffic/) on 2026-09-05.*
+
+This is the fourth part of a series I originally **[posted on the Dragos Blog](https://dragos.com/blog/20180306ThreatHuntingWithPythonPt4.html).**
+
+Working in the ICS information security space affords the opportunity to visit some cool critical infrastructure sites. From massive refineries that turn raw crude into diesel and gas to wind farms that harness the power of nature to generate electricity, it is easy to get caught in awe of the process itself and ignore some of the lower-level characteristics of control systems. This week, we will look at a protocol commonly overlooked by many but crucial to control system operation. The Tabular Data Stream (TDS) protocol is a core component of many data historians and plays a significant role in industrial networks. We will define TDS, where TDS typically lives, and how to hunt in TDS data.
+
+## What is Tabular Data Stream
+
+The Tabular Data Stream (TDS) protocol is an application layer protocol initially invented by a former enterprise software and services company, Sybase, in the mid-1980s. In 1986, Microsoft entered an agreement with Sybase to license Sybase’s DataServer application. The agreement between Sybase and Microsoft led to the first version of Microsoft SQL Server. In 1993, Microsoft ported SQL Server to Windows NT, adding the ability to use native management and networking features built into Windows. Microsoft hails the movement of SQL Server to Windows NT from the legacy IBM OS/2 environment as a considerable success. In 1994, Microsoft and Sybase parted ways.
+
+TDS continues to play a critical role in Microsoft SQL server today. Structured Query Language (SQL) queries and responses travel via TDS to and from Microsoft SQL servers. Additionally, Microsoft proves the ability to call user-defined functions, also referred to as remote procedure calls, using TDS. As with SMB, Microsoft has a [great reference page for TDS and how the protocol works here](https://msdn.microsoft.com/en-us/library/dd304523.aspx). Simply put, TDS is the database language of systems that rely on Microsoft SQL Server.
+
+## Where is TDS Used on Industrial Networks
+
+When we see TDS on industrial networks, generally the traffic is related to the operational historian. GE’s Proficy/iFix historian, Honeywell’s Uniformance Process History Database, and Schneider Electric’s Wonderware historian all rely on Microsoft SQL Server to some degree on the back end. It is worth pointing out that not everyone in the industry relies on Microsoft SQL server or other popular database back ends. OSIsoft has its proprietary database format, PI Archive, that does have an interface for communicating within the SQL standard.
+
+We often see TDS traffic on industrial networks between hosts that interact with the historian. Homegrown scripts that scrape metrics from historians we have seen in production environments also commonly use SQL APIs to query data. We have also widely seen TDS traffic between hosts that are expected to, and regularly interact, with the historian. Some historian manufacturers have written their own communications protocols for messages, but others still rely on TDS for SQL Server features.
+
+## Hunting in TDS
+
+To dive into TDS traffic, we used a Python module named pyshark. Pyshark is a wrapper for tshark and allows Python access to live network traffic or packet capture files. We imported pyshark and used the FileCapture method to read the packet capture file. We provided a display filter into the FileCapture method to filter the traffic to TDS only. If you are sniffing live network traffic, you can also use a Berkeley Capture Filter (bpf) in the file capture method.
+
+![](images/part4-1.png)
+
+We then read the TDS data from the packet capture and stored the data in a variable named cap\_data. We only looked at the observed SQL queries and remote procedure calls. We stored the codes for the different TDS traffic types and a few of the known remote procedures for easy conversion.
+
+![](images/part4-2.png)
+
+We then used a Pandas data frame to display the initial data. This table shows both RPC and query data. We split out the two different types of records and dove deeper into the dataset.
+
+![](images/part4-3.png)
+
+We then looked at just the SQL queries made. The output below displays all observed SQL queries and counts the number of times we see each.
+
+![](images/part4-4.png)
+
+As shown above, we observed two queries. The packet capture we used in this example came from Wireshark’s sample packet capture page. In industrial environments, we typically only see select statements. If you are hunting through TDS data on an industrial network, you should just see SQL statements related to the expected behavior of the examined device. If DROP TABLE, or CREATE USER statements appear, you should look deeper, as those SQL commands are likely malicious. A DROP TABLE command could be used to clear all the database out of the historian if an attacker wanted to wipe data as part of the action stage of the ICS kill chain, while a CREATE USER statement might be used by an attacker to add another user. There are many other techniques an attacker could use against SQL server. Knowing your environment and what hosts should be performing which SQL statement behaviors is key to identifying attacks against a historian.
+
+We then pivoted to remote procedure calls. As previously mentioned, TDS allows users to call functions via RPC. One of the classic remote code exploitation vulnerabilities from years ago, known as [MS 03-026 or RPC DCOM](https://www.rapid7.com/db/modules/exploit/windows/dcerpc/ms03_026_dcom), exploited a buffer overflow in an RPC function. This vulnerability did not directly relate to TDS, but it is worth mentioning that functions that can be called remotely via RPC have been exploited in the past to gain access to Windows machines.
+
+Next, we examined how to look at the functions being called via TDS. This allows you to gain visibility into what is typical for your network and also check for function calls following any future vulnerability announcements.
+
+![](images/part4-5.png)
+
+We first looked at the nine unique procedure names our dataset contains. Knowing what is malicious or benign will require you to know about your environment and what might be talking to the historian or SQL server. If you use baselining in your environment, called functions are a good candidate to add to your baseline.
+
+![](images/part4-6.png)
+
+Next, we looked at stored procedures. These procedures are a part of the TDS specification. As with the named procedures, you should consider the behavioral context of the devices querying the SQL server.
+
+![](images/part4-7.png)
+
+If you do find something suspicious, you can drill down into the data and display information that might assist your investigation. The first cell displays a call to the p\_getBogusData function. We can see the time the function was called, the source host, and the destination host that received the function call. The second box shows calls to either p\_SetBogusSample or sp\_execute sq. You can sort and filter any data using the features built into Pandas.
+
+## Wrapping Everything Up
+
+Microsoft SQL Server is a workhorse protocol for many data historians on industrial networks. TDS is the language Microsoft SQL Server speaks. Understanding the queries and remote procedure calls on your network and monitoring for malicious queries and procedure calls are two activities you should perform at a minimum for TDS traffic present in your network environment. We used Python, Jupyter Notebooks, and Pandas for our analysis, but you can use these techniques with your hunting platform of choice.
+
+Have any questions or want to chat more? Reach out to me on twitter via [@dan\_gunter.](https://twitter.com/dan_gunter)
+
+## References:
+
+Microsoft TDS Protocol Reference: <https://msdn.microsoft.com/en-us/library/dd304523.aspx>
+
+Background on MS SQL Server: [https://news.microsoft.com/…sql-server-climbs-to-new-heights/](https://news.microsoft.com/2001/06/28/building-the-billion-dollar-database-microsoft-sql-server-climbs-to-new-heights/)
+
+MS 03-026 Rapid 7 Reference Page: [https://www.rapid7.com/…/ms03\_026\_dcom](https://www.rapid7.com/db/modules/exploit/windows/dcerpc/ms03_026_dcom)
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new file mode 100644
index 0000000..d23471b
--- /dev/null
+++ b/pyproject.toml
@@ -0,0 +1,39 @@
+[project]
+name = "blog-code"
+version = "2.0.0"
+description = "Jupyter notebooks from the Threat Hunting with Python series at dgunter.com"
+readme = "README.md"
+license = "MIT"
+license-files = ["LICENSE"]
+requires-python = ">=3.10"
+dependencies = [
+    "jupyter>=1.1",
+    "matplotlib>=3.9",
+    "pandas>=2.2",
+    "parsezeeklogs>=3.0.1",
+    "pyshark>=0.6",
+]
+
+[dependency-groups]
+dev = [
+    "nbconvert>=7.16",
+    "ruff>=0.6",
+]
+
+[tool.ruff]
+line-length = 100
+target-version = "py310"
+extend-exclude = ["*.md"]
+
+[tool.ruff.lint]
+select = ["E", "F", "W", "I", "UP", "B"]
+# Notebooks: expressions on their own line are how cells display values.
+ignore = ["B018"]
+
+[tool.ruff.lint.per-file-ignores]
+# The 2017 notebooks are kept as written, long user-agent strings included.
+"Bro HTTP Log Analysis/Bro HTTP Nmap Indicator Detection.ipynb" = ["E501"]
+"Bro HTTP Log Analysis/Detecting Nmap Behavior with Bro HTTP Logs.ipynb" = ["E501"]
+
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+package = false
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..bd4a5f5
--- /dev/null
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From 8d575bdb9abbb47d5a8b09c059fbff898f8ace5a Mon Sep 17 00:00:00 2001
From: dgunter <dgunter@users.noreply.github.com>
Date: Sat, 5 Sep 2026 17:14:01 -0500
Subject: [PATCH 2/2] CI: install the current stable Wireshark from the project
 PPA

Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com>
---
 .github/workflows/notebooks.yml | 5 ++++-
 README.md                       | 5 +++--
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/.github/workflows/notebooks.yml b/.github/workflows/notebooks.yml
index a6e5819..02446f2 100644
--- a/.github/workflows/notebooks.yml
+++ b/.github/workflows/notebooks.yml
@@ -14,10 +14,13 @@ jobs:
       - uses: astral-sh/setup-uv@v6
         with:
           enable-cache: true
-      - name: Install tshark for the TDS notebook
+      - name: Install the current stable tshark for the TDS notebook
+        # Ubuntu's own package lags; the Wireshark project's PPA tracks the latest stable release.
         run: |
+          sudo add-apt-repository -y ppa:wireshark-dev/stable
           sudo apt-get update -q
           sudo DEBIAN_FRONTEND=noninteractive apt-get install -y -q tshark
+          tshark --version | head -1
       - name: Install the Python environment
         run: uv sync --frozen
       - name: Lint code cells
diff --git a/README.md b/README.md
index 7cfbffa..a0548bf 100644
--- a/README.md
+++ b/README.md
@@ -41,8 +41,9 @@ the posts.
 ## Running them
 
 Requires Python 3.10 or newer and [uv](https://docs.astral.sh/uv/). The TDS
-notebook also needs `tshark` from Wireshark on your `PATH`
-(`brew install wireshark` on macOS, `apt install tshark` on Debian and Ubuntu).
+notebook also needs `tshark` from a current Wireshark on your `PATH`: 4.6 is
+what it was verified with (`brew install wireshark` on macOS; on Ubuntu the
+`ppa:wireshark-dev/stable` PPA carries the latest release, as CI uses).
 
 ```bash
 uv sync