2021/04/19 by Ashutosh Dutta, Dutta, Ashutosh, Ehab Al‐Shaer +3 · 1 citation
Computer Science · Engineering · #Artificial Intelligence (cs.AI) #Computer Science and Game Theory (cs.GT) #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Information and Cyber Security #Safety Systems Engineering in Autonomy #Smart Grid Security and Resilience
paper · pdf · doi:10.48550/arxiv.2104.08994
openalex publication_date 2021/04/19 · openalex created_date 2021/05/10 · openalex updated_date 2026/07/28
With the increasing system complexity and attack sophistication, the necessity of autonomous cyber defense becomes vivid for cyber and cyber-physical systems (CPSs). Many existing frameworks in the current state-of-the-art either rely on static models with unrealistic assumptions, or fail to satisfy the system safety and security requirements. In this paper, we present a new hybrid autonomous agent architecture that aims to optimize and verify defense policies of reinforcement learning (RL) by incorporating constraints verification (using satisfiability modulo theory (SMT)) into the agent's decision loop. The incorporation of SMT does not only ensure the satisfiability of safety and security requirements, but also provides constant feedback to steer the RL decision-making toward safe and effective actions. This approach is critically needed for CPSs that exhibit high risk due to safety or security violations. Our evaluation of the presented approach in a simulated CPS environment shows that the agent learns the optimal policy fast and defeats diversified attack strategies in 99% cases.