New Approaches to Detection and Secure Control for Cyber-physical Systems Against False Data Injection Attacks

This study focuses on detecting and defending against false data injection attacks (FDIAs) on cyber-physical systems (CPSs). Firstly, recognizing the stealthy nature of FDIAs, deep reinforcement learning (DRL) is employed to design an automatic FDIA detector capable of learning different attack patterns. To enhance the robustness of the DRL algorithm, a new detection approach based on the improved proximal policy optimization (PPO) algorithm is devised to adapt to various FDIA modes. Secondly, to counteract the impact of FDIAs, an event-triggered model predictive control (MPC) approach is proposed to ensure the system swiftly returns to a stable state after being subjected to FDIAs. Lastly, the effectiveness of the proposed attack detector based on the DRL algorithm and the event-triggered model predictive controller is validated through a simulation example.