False Data Injection Attack Detection for Virtual Coupling Heavy-Haul Trains

The cooperative control of the virtual coupling system for heavy-haul trains relies on information interaction with each train, resulting in various forms of network attacks posing a huge threat to the reliable transmission of information and safe and efficient operation of the virtual coupling system for heavy-haul trains. To address this problem, this research introduces an reservoir computing-Based method to investigate the strategy for detecting false data injection attacks (FDIAs) in the heavy-haul train virtual coupling system. Initially, a cyber-physical model of the virtual coupling system is established. Subsequently, a cooperative control law is devised for the virtual coupling system, and the potential impact of FDIAs on the system is analyzed. The proposed method learns to reconstruct normal signal data and identifies anomalies according to reconstruction errors. Its effectiveness in identifying attacks on the virtual coupling system for heavy-haul trains is then validated through simulations.