Analyzing BotNets with Suricata & Machine Learning

Since the official rollout at the year’s. conf of the Machine Learning Toolkit(MLTK), Splunkers have been pursing some interesting use cases ranging from IT operations, planning, security and business analytics. Those use cases barely scratch the surface of what is possible with machine learning and Splunk. As an example, I will use the machine learning toolkit and data collected from Suricata to analyze botnet populations. This population analysis will be used to create a model for predicting the Mirai botnet based on network features.

Suricata

Suricata is an open source threat detection engine, which can be run in passive mode for intrusion detection or inline for intrusion prevention. My lab environment is configured for intrusion detection, meaning Suricata will not …

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Analyzing the Mirai Botnet with Splunk

On September 20th, the largest Distributed Denial of Service attack ever recorded targeted security researcher Brian Krebs. This attack was made up of Internet of Things (IoT) devices such as cameras, wireless controllers and internet enabled devices peaking at 400,000 total. Now dubbed the “Mirai botnet”, these devices scanned the internet for devices running telnet and SSH with default credentials, infecting them and further propagating. The source code for the botnet has since leaked to GitHub, where further analysis is underway by security researchers.

During the infection time period, I happened to be running a honeypot and captured some infection attempts on my own system. Using Suricata and /var/log/secure.log I can correlate invalid login attempts associated with Mirai with malicious …

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