CyberBattleSim allows for the training of automated agents, and provides a Python-based OpenAI Gym interface for that. In the simulated environments, defenders can leverage reinforcement learning algorithms and set up various cybersecurity challenges. Helps AI & ML for providing high level abstraction simulation of network & security.

Reinforcement learning, is a type of machine learning that teaches autonomous agents to make decisions based on the interaction with the environment: agents improve strategies through repeated experience. It involves agents to play role of attacker and defender, where attacker mulls to steal and defender pulls to block the cation

CyberBattleSim employs OpenAI Gym for building interactive environments, and focuses on the lateral movement phase of a cyber-attack. The project simulates a fixed network with predefined vulnerabilities that the attacker model can exploit for lateral movement, while a defender agent seeks to detect the attacker and contain the intrusion. No security exploit takes place in the simulation

The simulated computer network consists of systems running multiple platforms and aims to illustrate how the use of the latest operating systems and keeping them updated can deliver improved protections. Using the Gym interface, defenders can instantiate automated agents and then analyze their evolution in the environment. Based on observation it has to react

Microsoft says CyberBattleSim has a highly abstract nature and cannot be applied to real-world systems, which provides protection against the nefarious use of the trained automated agents.