Journal
IEEE TRANSACTIONS ON CYBERNETICS
Volume 51, Issue 9, Pages 4648-4660Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2020.3006871
Keywords
Actuators; State estimation; Games; Security; Communication networks; Symmetric matrices; Cyber-physical systems; Attack estimation; cyber-physical security; differential games; mitigation; reinforcement learning (RL)
Categories
Funding
- NSF [ECCS-1903781, SAS-1849198]
- NATO [SPS G5176]
- ONR Minerva [N00014-18-1-2160]
- NSF CAREER [CPS-1851588]
- Air Force Office of Scientific Research [FA9550-20-1-0038]
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This article introduces a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. By using observer-based estimators to detect attacks and introducing a threat-detection level function, the system can operate under attack conditions and learn secure control strategies.
In this article, we develop a learning-based secure control framework for cyber-physical systems in the presence of sensor and actuator attacks. Specifically, we use a bank of observer-based estimators to detect the attacks while introducing a threat-detection level function. Under nominal conditions, the system operates with a nominal-feedback controller with the developed attack monitoring process checking the reliance of the measurements. If there exists an attacker injecting attack signals to a subset of the sensors and/or actuators, then the attack mitigation process is triggered and a two-player, zero-sum differential game is formulated with the defender being the minimizer and the attacker being the maximizer. Next, we solve the underlying joint state estimation and attack mitigation problem and learn the secure control policy using a reinforcement-learning-based algorithm. Finally, two illustrative numerical examples are provided to show the efficacy of the proposed framework.
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