False Data Injection Attack Detection for Virtual Coupling Systems of Heavy-Haul Trains: A Deep Learning Approach

Abstract

Cooperative control for virtual coupling systems of multiple heavy-haul trains can improve the safety and efficiency of heavy-haul railway transportation. However, the false data injection attack for the virtual coupling system is a serious obstacle, which will lead to imprecise train operation control. To address this issue, a deep learning-based false data injection attack (FDIA) detection for virtual coupling systems of heavy-haul trains is proposed. First, the cyber-physical model of the virtual coupling system is established. Second, a cooperative control law is designed for the virtual coupling system, and the effects of the FDIA on the virtual coupling system is analyzed. Then, the unsupervised autoencoder method is introduced to achieve the false data injection attack detection. The autoencoder network model is trained with normal operation data and tested with abnormal operation data. The performance of the proposed method is verified in four different simulation scenarios: normal case, velocity attack case, position attack case, and joint attack case. Simulation results show that the proposed method can effectively increase the detection accuracy and reduce the error rate with other supervised methods.