Game-Theoretic Analysis of Autonomous Cyber-Defence Agents
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🛡️🎮 Advancements in Autonomous Cyber-Defence Agents through Game Theory. The rise of sophisticated cyber-attacks necessitates the development of robust autonomous cyber-defence (ACD) agents. This article presents an empirical game-theoretic analysis utilizing deep reinforcement learning (DRL) and the double oracle (DO) algorithm to enhance ACD effectiveness. The authors introduce a potential-based reward shaping method to streamline the learning process for these agents, addressing the computational challenges involved. Additionally, they propose an extension of the DO framework to incorporate multiple response oracles (MRO), facilitating a comprehensive evaluation of various ACD strategies. This research aims to improve the resilience and adaptability of ACD agents in the face of evolving cyber threats.
