Abstract
The static nature of cyber defense systems gives attackers a sufficient amount of time to explore and further exploit the vulnerabilities of information technology systems. In this paper, we investigate a problem where multiagent sys-tems sensing and acting in an environment contribute to adaptive cyber defense. We present a learning strategy that enables multiple agents to learn optimal poli-cies using multiagent reinforcement learning (MARL). Our proposed approach is inspired by the multiarmed bandits (MAB) learning technique for multiple agents to cooperate in decision making or to work independently. We study a MAB approach in which defenders visit a system multiple times in an alternating fash-ion to maximize their rewards and protect their system. We find that this game can be modeled from an individual player's perspective as a restless MAB problem. We discover further results when the MAB takes the form of a pure birth process, such as a myopic optimal policy, as well as providing environments that offer the necessary incentives required for cooperation in multiplayer projects.