Python for Dark Web OSINT: Automate Threat Monitoring

Enhance your OSINT toolkit! Learn how to use Python to automate monitoring of dark web forums, leak sites, and marketplaces for actionable threat intelligence.

This article is also published on my Medium page.

In my previous article, “OSINT: Persistent Threat Monitoring with Google Programmable Search Engines,” I explored the value of open-source intelligence (OSINT) techniques for tracking threats. Since then, I’ve received many questions about how to apply similar methods to the dark web. While Google PSEs can’t index the dark web, we can leverage Python to scrape and automate threat intelligence activities in this shadowy domain.

https://publication.osintambition.org/osint-persistent-threat-monitoring-with-google-programmable-search-engines-959a141831c8

What is a Programmable Search Engine (PSE)? Programmable Search Engines allow users to create customized search engines for websites, forums or blogs, including the option to search web pages and images.

Understanding the Dark Web and Tor

The dark web is a secretive part of the internet, deliberately hidden from the reach of mainstream search engines. To explore this realm, you’ll need specialized software like the Tor browser. Tor (short for The Onion Router) cleverly disguises your IP address by bouncing your internet traffic through a network of anonymous relays. This makes it much harder for anyone to track your online activities.

Now that we understand the basics, let’s dive into how we can harness the power of Python to automate our interactions with the dark web. Of course, we’ll start by ensuring our setup is secure. For this guide, I’ll be using CSI Linux, a distribution tailored for digital forensics. Feel free to follow along by setting up your own CSI Linux virtual box or use your preferred distribution. If you’d like help setting up CSI Linux, check out my setup guide below to get you started.

https://publication.osintambition.org/osint-investigations-made-simple-installing-csi-linux-on-virtualbox-61fb280c9009

Setting Up Your Environment

To tap into the power of the dark web with Python, let’s get your system ready. We’ll need the Tor browser for anonymity and a few Python libraries to handle the scripting magic.

1. Install Tor

CSI Linux Users: You’re in luck — Tor comes preinstalled! Skip this step.
Other Distributions: Use your system’s package manager to install Tor (e.g., sudo apt install tor on Debian-based Linux).

Figure 1. Terminal window with a command to install the Tor browser using the apt package manager on a Unix-like OS.

2. Configure Tor

Open the Tor configuration file (/etc/tor/torrc) in your favorite text editor.
Locate the line ControlPort 9051 and remove the “#” symbol (if present) to uncomment it.
Find the CookieAuthentication option and set it to 0. This simplifies our setup for now.

Figure 2. A screenshot of an open GNU nano text editor displaying configuration settings for Tor, including ports and authentication options.

Important Security Note: Leave your setup without a password only for experimentation. Always add a strong control port password for production environments.

3. Python Power-Up

Now, let’s turn Python into our key for unlocking the secrets of the dark web:

pip install requests_tor (while you’re at it, you may as well install beautifulsoup4; we will need it later).

Figure 3. Terminal output for the installation of the requests_tor Python package via pip.

Next, let’s quickly verify that Tor is active and your IP address is masked before we start scripting our dark web adventure.

Figure 4. A terminal display showing a user editing the Tor configuration file, restarting the Tor service, and using curl with Torify to fetch an IP address.

Perfect! Looks like the setup is a success — my IP address says I’m in Romania, even though I’m definitely not. Time to put our dark web exploration skills to work!

Figure 5. An interface displaying details and location of an IP address identified as a Tor exit node in Bucharest, Romania.

For a quick sanity check, let’s reach into the dark web with the classic curl command. Who knows what hidden data we might uncover…

Figure 6. Terminal command using curl to access an .onion address via SOCKS5 proxy.

…and the HTML response confirms it! Our setup is working flawlessly — time to unleash the power of Python scripting for some serious automation.

Figure 7. Terminal screen showing HTML code of a web post about a stolen crypto wallet.

Automate Dark Web Threat Monitoring with Python Script and Requests-Tor

Now that we’ve configured our environment, let’s put the power of Python to work. Here’s a script that utilizes the requests_tor library to scrape dark websites and hunt for those tell-tale keywords seamlessly:

Figure 8. Python script in a text editor for scraping .onion sites using RequestsTor.

Explanation of the Code

Beautiful Soup Integration: Added the bs4 library to parse the HTML structure of scraped pages, allowing for more refined searches.
Error Handling: We use a try…except block for more graceful error handling, printing informative messages if scraping fails.
Function-Based Approach: Modularized the scraping and searching logic into a reusable scrape_and_search function.
Structured Data: We employ a list of tuples (sites) to hold URL and name pairs, making the code more organized.
Keyword List: Define a keyword list to easily manage the terms you want to search for.
Concise Output: Set up a clear output indicating the scraped site and any found keywords.

Automating Threat Monitoring

To turn this into a threat monitoring solution, you could:

Schedule: Use tools like cron (on Linux) or Task Scheduler (on Windows) to run your script at regular intervals.
Alerts: Integrate with notification services (email, SMS, or messaging platforms) to receive alerts when specific events occur.

Advanced Techniques and Considerations

Stem Library: Use the stem library for more granular control over Tor circuits and relays.
Data Storage and Analysis: Store collected data in a structured format (database or CSV) for further analysis and historical tracking.
OpSec: Be mindful of operational security. Avoid leaving sensitive traces on your system and always use a dedicated, isolated environments for dark web monitoring.

Ethical Considerations

Responsible Engagement: Use dark web intelligence gathering for defensive purposes. Avoid any actions that could put yourself or others at risk.
Respect Privacy: Even when monitoring threat actors, be mindful of the privacy of individuals who may be caught up in these activities.

Conclusion

Combining Python’s scripting power with Tor’s anonymity features, we’ve unlocked a powerful tool for monitoring the dark web and gathering valuable threat intelligence. This article has provided a foundation, but the possibilities for customization and refinement are endless. Have you developed your own unique dark web monitoring scripts or techniques? Share your insights within the OSINT and cybersecurity communities — collaboration is key to staying ahead of evolving threats. In future articles, we’ll delve deeper into advanced data analysis techniques to extract even more meaningful insights from the dark web data you collect.

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Ervin Zubic