Defense Strategy Against False Data Injection Attacks on Cyber–Physical System for Vehicle–Grid Based on KNN-GAE

Qiuyan Li, Dawei Song, Yuanyuan Wang, Di Wang (), Weijian Tao and Qian Ai
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Qiuyan Li: State Grid Henan Electric Power Company Economic and Technological Research Institute, Zhengzhou 450052, China
Dawei Song: State Grid Henan Electric Power Company Economic and Technological Research Institute, Zhengzhou 450052, China
Yuanyuan Wang: State Grid Henan Electric Power Company Economic and Technological Research Institute, Zhengzhou 450052, China
Di Wang: School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Weijian Tao: School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Qian Ai: School of Electrical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Energies, 2025, vol. 18, issue 19, 1-24

Abstract: With the in-depth integration of electric vehicles (EVs) and smart grids, the Cyber–Physical System for Vehicle–Grid (CPSVG) has become a crucial component of power systems. However, its inherent characteristic of deep cyber–physical coupling also renders it vulnerable to cyberattacks, particularly False Data Injection Attacks (FDIAs), which pose a severe threat to the safe and stable operation of the system. To address this challenge, this paper proposes an FDIA defense method based on K-Nearest Neighbor (KNN) and Graph Autoencoder (GAE). The method first employs the KNN algorithm to locate abnormal data in the system and identify the attacked nodes. Subsequently, Graph Autoencoder is utilized to reconstruct the tampered and contaminated data with high fidelity, restoring the accuracy and integrity of the data. Simulation verification was conducted in a typical vehicle–grid interaction system scenario. The results demonstrate that, compared with various scenarios such as no defense, traditional detection mechanisms, and only location-based data elimination, the proposed KNN-GAE method can more accurately identify and repair all attacked data. It provides reliable data input that is closest to the true values for subsequent state estimation, thereby significantly enhancing the system’s state awareness capability and operational stability after an attack. This study offers new insights and effective technical means for ensuring the security defense of the Vehicle–Grid Interaction Cyber–Physical System.

Keywords: cyber–physical system for vehicle–grid; false data injection attack; k-nearest neighbors; graph autoencoder; state estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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