A Learning-Based Passive Resilient Controller for Cyber-Physical Systems: Countering Stealthy Deception Attacks and Complete Loss of Actuators Control Authority

Cyber-physical systems (CPSs) are increasingly vulnerable to cyber-attacks due to their integral connection between cyberspace and the physical world, which is augmented by Internet connectivity. This vulnerability necessitates a heightened focus on developing resilient control mechanisms for CPSs. However, current observer-based active compensation resilient controllers exhibit poor performance against stealthy deception attacks (SDAs) due to the difficulty in accurately reconstructing system states because of the stealthy nature of these attacks. Moreover, some non-active compensation approaches are insufficient when there is a complete loss of actuator control authority. To address these issues, we introduce a novel learning-based passive resilient controller (LPRC). Our approach, unlike observer-based state reconstruction, shows enhanced effectiveness in countering SDAs. We developed a safety state set, represented by an ellipsoid, to ensure CPS stability under SDA conditions, maintaining system trajectories within this set. Additionally, by employing deep reinforcement learning (DRL), the LPRC acquires the capacity to adapt and diverse evolving attack strategies. To empirically substantiate our methodology, various attack methods were compared with current passive and active compensation resilient control methods to evaluate their performance.