Hardening cyber physical assets is both crucial and labor-intensive. Recently, Machine Learning (ML) in general and Reinforcement Learning RL) more specifically has shown great promise to automate tasks that otherwise would require significant human insight/intelligence. The development of autonomous RL agents requires a suitable training environment that allows us to quickly evaluate various alternatives, in particular how to arrange training scenarios that pit attackers and defenders against each other. CyberBattleSim is a training environment that supports the training of red agents, i.e., attackers. We added the capability to train blue agents, i.e., defenders. The paper describes our changes and reports on the results we obtained when training blue agents, either in isolation or jointly with red agents. Our results show that training a blue agent does lead to stronger defenses against attacks. In particular, training a blue agent jointly with a red agent increases the blue agent's capability to thwart sophisticated red agents.
@article{arxiv.2304.11052,
title = {A Multiagent CyberBattleSim for RL Cyber Operation Agents},
author = {Thomas Kunz and Christian Fisher and James La Novara-Gsell and Christopher Nguyen and Li Li},
journal= {arXiv preprint arXiv:2304.11052},
year = {2023}
}
Comments
To appear in Proceedings of the 2022 International Conference on Computational Science and Computational Intelligence