Gamifying ML for stronger security - CyberBattleSim (Simulator)

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Blog: Gamifying machine learning for stronger security and AI models - Microsoft Security

To stay ahead of adversaries, who show no restraint in adopting tools and techniques that can help them attain their goals, Microsoft continues to harness AI and machine learning to solve security challenges. One area we’ve been experimenting on is autonomous systems. In a simulated enterprise network, we examine how autonomous agents, which are intelligent systems that independently carry out a set of operations using certain knowledge or parameters, interact within the environment and study how reinforcement learning techniques can be applied to improve security.

Today, we’d like to share some results from these experiments. We are open sourcing the Python source code of a research toolkit we call CyberBattleSim, an experimental research project that investigates how autonomous agents operate in a simulated enterprise environment using high-level abstraction of computer networks and cybersecurity concepts. The toolkit uses the Python-based OpenAI Gym interface to allow training of automated agents using reinforcement learning algorithms.

The code is available here: microsoft/CyberBattleSim

CyberBattleSim focuses on threat modeling the post-breach lateral movement stage of a cyberattack. The environment consists of a network of computer nodes. It is parameterized by a fixed network topology and a set of predefined vulnerabilities that an agent can exploit to laterally move through the network. The simulated attacker’s goal is to take ownership of some portion of the network by exploiting these planted vulnerabilities. While the simulated attacker moves through the network, a defender agent watches the network activity to detect the presence of the attacker and contain the attack.