Automated Adversary-in-the-Loop Cyber-Physical Defense Planning

نویسندگان

چکیده

Security of cyber-physical systems (CPS) continues to pose new challenges due the tight integration and operational complexity cyber physical components. To address these challenges, this article presents a domain-aware, optimization-based approach determine an effective defense strategy for CPS in automated fashion—by emulating strategic adversary loop that exploits system vulnerabilities, interconnection CPS, dynamics Our builds on adversarial decision-making model based Markov Decision Process (MDP) determines optimal (discrete) (continuous) attack actions over graph. The planning problem is modeled as non-zero-sum game between defender. We use model-free reinforcement learning method solve adversary’s function strategy. then employ Bayesian optimization (BO) find approximate best-response defender harden network against resulting policy. This process iterated multiple times improve both players. demonstrate effectiveness our ransomware-inspired graph with smart building process. Numerical studies show converges Nash equilibrium various defender-specific costs hardening.

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ژورنال

عنوان ژورنال: ACM Transactions on Cyber-Physical Systems

سال: 2023

ISSN: ['2378-962X', '2378-9638']

DOI: https://doi.org/10.1145/3596222