An experimentation platform for training and researching automated agents in abstract simulated enterprise network environments using reinforcement learning.
CyberBattleSim is an experimentation and research platform that simulates abstract enterprise network environments to investigate the interaction of automated cybersecurity agents. It enables training of reinforcement learning agents to perform lateral movement attacks while a defender agent attempts detection and mitigation. The platform provides a safe, simplified abstraction for studying autonomous agent behavior without real-world network risks.
Cybersecurity researchers, machine learning practitioners, and academic institutions studying autonomous agent interactions, reinforcement learning applications in security, and attack/defender dynamics in simulated environments.
It offers a unique, abstract simulation environment specifically designed for safe research on autonomous cybersecurity agents, with built-in attacker-defender dynamics and extensible network models that avoid real-world applicability risks while enabling focused experimentation with state-of-the-art AI algorithms.
An experimentation and research platform to investigate the interaction of automated agents in an abstract simulated network environments.
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The simulation is intentionally simplistic to prevent nefarious use and focus on specific security aspects, as stated in the README's philosophy, enabling safe experimentation without real-world risks.
It provides a Python-based Open AI Gym interface, allowing for straightforward training of reinforcement learning agents on cybersecurity scenarios, as highlighted in the key features.
Includes a basic stochastic defender agent that detects and mitigates attacks, facilitating the study of interaction between automated agents, which is core to the project's goals.
Supports custom network topologies and vulnerability sets, enabling varied experimentation, as mentioned in the extensible environments feature and contribution ideas.
The README admits that modeling actual network traffic was not necessary, making it unsuitable for research requiring realistic network behavior or detailed cyber-attack simulations.
Documentation strongly recommends Linux or WSL, with Windows support unsupported, involving multiple steps like conda installation and environment activation, which can be a barrier to entry.
The README highlights challenges in training agents that can store and retrieve credentials, a key limitation for advanced reinforcement learning applications in cybersecurity scenarios.