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

Author

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  • Xiaoyong Bo

    (Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
    Electrical and Information Engineering College, Jilin Agricultural Science and Technology University, Jilin 132101, China
    Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China)

  • Zhaoyang Qu

    (Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
    Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China)

  • Lei Wang

    (Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
    Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China)

  • Yunchang Dong

    (Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
    Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China)

  • Zhenming Zhang

    (Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China
    Jilin Province Engineering Technology Research Center of Power Big Data Intelligent Processing, Jilin 132012, China)

  • Da Wang

    (Electrical Engineering College, Northeast Electric Power University, Jilin 132012, China)

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.

Suggested Citation

  • Xiaoyong Bo & Zhaoyang Qu & Lei Wang & Yunchang Dong & Zhenming Zhang & Da Wang, 2022. "Active Defense Research against False Data Injection Attacks of Power CPS Based on Data-Driven Algorithms," Energies, MDPI, vol. 15(19), pages 1-23, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7432-:d:938094
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    References listed on IDEAS

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    1. Hassan Haes Alhelou & Mohamad Esmail Hamedani-Golshan & Takawira Cuthbert Njenda & Pierluigi Siano, 2019. "A Survey on Power System Blackout and Cascading Events: Research Motivations and Challenges," Energies, MDPI, vol. 12(4), pages 1-28, February.
    2. Lai, Kexing & Illindala, Mahesh & Subramaniam, Karthikeyan, 2019. "A tri-level optimization model to mitigate coordinated attacks on electric power systems in a cyber-physical environment," Applied Energy, Elsevier, vol. 235(C), pages 204-218.
    3. Zhaoyang Qu & Yunchang Dong & Nan Qu & Lei Wang & Yang Li & Yu Zhang & Sylvere Mugemanyi, 2019. "Survivability Evaluation Method for Cascading Failure of Electric Cyber Physical System Considering Load Optimal Allocation," Mathematical Problems in Engineering, Hindawi, vol. 2019, pages 1-15, July.
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    Cited by:

    1. Smitha Joyce Pinto & Pierluigi Siano & Mimmo Parente, 2023. "Review of Cybersecurity Analysis in Smart Distribution Systems and Future Directions for Using Unsupervised Learning Methods for Cyber Detection," Energies, MDPI, vol. 16(4), pages 1-24, February.

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