Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms

Xiaoyong Bo (), Zhaoyang Qu, Lei Wang, Yunchang Dong, Zhenming Zhang and Da Wang
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Xiaoyong Bo: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
Zhaoyang Qu: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
Lei Wang: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
Yunchang Dong: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
Zhenming Zhang: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
Da Wang: Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China

Energies, 2022, vol. 15, issue 19, 1-23

Abstract: The terminal equipment interconnection and the network communication environment are complex in power cyber–physical systems (CPS), and the frequent interaction between the information and energy flows aggravates the risk of false data injection attacks (FDIAs) in the power grid. This paper proposes an active defense framework against FDIAs of power CPS based on data-driven algorithms in order to ensure that FDIAs can be efficiently detected and processed in real-time during power grid operation. First, the data transmission scenario and false data injection forms of power CPS were analyzed, and the FDIA mathematical model was expounded. Then, from a data-driven perspective, the algorithm improvement and process design were carried out for the three key links of data enhancement, attack detection, and data reconstruction. Finally, an active defense framework against FDIAs was proposed. The example analysis verified that the method proposed in this paper could effectively detect FDIAs and perform data reconstruction, providing a new idea for the active defense against FDIAs of power CPS.

Keywords: data-driven; power cyber–physical systems; false data injection attacks; active defense; variational auto-encoder (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: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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