Analyzing Multi-Agent Reinforcement Learning and Coevolution in Cybersecurity

Cybersecurity simulations can offer deep insights into the behavior of agents in the battle to secure computer systems. We build on existing work modeling the competition between an attacker and defender on a network architecture in a zero-sum game using a graph database linking cybersecurity attack patterns, vulnerabilities, and software. We apply coevolution to this challenging environment, and in a novel modeling approach for this problem, interpret each population as a distribution over fixed strategies to form a mixed strategy Nash equilibrium. We compare the results to solutions generated by multi-agent reinforcement learning and show that evolutionary methods demonstrate a considerable degree of robustness to parameter misspecification in this environment.