\n",
@@ -154,7 +211,7 @@
" | 1433 | \n",
" 1 | \n",
" SQL Batch | \n",
- " set transaction isolation level read committ... | \n",
+ " set transaction isolation level read committ... | \n",
" | \n",
" | \n",
" \n",
@@ -258,7 +315,7 @@
" 1433 | \n",
" 1 | \n",
" SQL Batch | \n",
- " set transaction isolation level read committ... | \n",
+ " set transaction isolation level read committ... | \n",
" | \n",
" | \n",
" \n",
@@ -455,7 +512,7 @@
" RPC | \n",
" | \n",
" | \n",
- " p_SaveExample | \n",
+ " | \n",
" \n",
" \n",
" | 24 | \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": [
"\n",
- "\n",
"
\n",
" \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": [
"\n",
- "\n",
"
\n",
" \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:
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+Hopefully, the user agent named “Mozilla/5.0 (compatible; Nmap Scripting Engine; )” 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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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
--- /dev/null
+++ 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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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.
+
+
+
+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:
+
+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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diff --git a/pyproject.toml b/pyproject.toml
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"]
+
+[tool.uv]
+package = false
diff --git a/uv.lock b/uv.lock
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+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
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