False Data Injection Attack Detection of Cyber-Physical Charging Systems Based on Time-Frequency Analysis

Distributed charging systems play a critical role in the operation of supercapacitor electric vehicles. During the charging process, multiple charging modules rely on a communication network to achieve information sharing and interaction. If an external attack occurs on the network, the system will be in danger. To solve this problem, this paper proposes an anomaly detection mechanism based on real-time feature extraction to process measurement data. By analyzing the delayed effect of communication signal propagation, a method for identifying component faults and false data injection attacks (FDIA) is proposed. Moreover, the effectiveness and feasibility of the proposed distributed detection method are verified through experiments. Multiple experiments have consistently demonstrated that the proposed method outperforms existing methods FDIA detection.