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A False Data Injection Attack Detection Method for Cooperative Charging Systems
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AI
DOI：10.1109/TIA.2022.3154688.png)
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Abstract
En 中文
Supercapacitors have been widely used in public transportation for the advantages of high power density and extremely long lifetime. Owing to the high-power charging requirements of supercapacitors, the multicharger cooperative charging method has been applied to suppress the current imbalance among chargers. However, the cooperative charging system is vulnerable to cyber attacks with false data injections, which degrades the reliability of the charging system. To address this challenge, in this article, a detection method for a false data injection attack (FDIA) is proposed for the cooperative charging system. First, we introduce the cooperative charging system and analyze the possible cyber attacks. Then, an FDIA detection method is proposed based on a Kalman filter and the time-frequency features of chargers' currents. A three-charger laboratory platform is built to verify the effectiveness of the proposed method. Extensive experimental results show that the proposed method can detect the FDIA effectively when compared with the existing methods.
Keywords:
Kalman filters
Feature extraction
Time-frequency analysis
Supercapacitors
Time-domain analysis
Wireless communication
Physical layer
Charging system
cooperative control
detection
false data injection attack (FDIA)
supercapacitor
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