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Earth-Mover-Distance-Based Detection of False Data Injection Attacks in Smart Grids

Author

Listed:
  • Zhengwei Qu

    (State Key Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, Beijing 100192, China
    School of Electrical Engineering, Yanshan University, Qinghuangdao 066004, China)

  • Jingchuan Yang

    (School of Electrical Engineering, Yanshan University, Qinghuangdao 066004, China)

  • Yansheng Lang

    (State Key Laboratory of Power Grid Safety and Energy Conservation, China Electric Power Research Institute, Beijing 100192, China)

  • Yunjing Wang

    (School of Electrical Engineering, Yanshan University, Qinghuangdao 066004, China)

  • Xiaoming Han

    (School of Electrical Engineering, Yanshan University, Qinghuangdao 066004, China)

  • Xinyue Guo

    (School of Electrical Engineering, Yanshan University, Qinghuangdao 066004, China)

Abstract

The high integration of power information physical system improves the efficiency of power transmission, but it also brings new threats to power grid. False data injection attacks can use traditional bad data to detect vulnerabilities and maliciously tamper with measurement data to affect the state estimation results. In order to achieve a higher security level for power systems, we propose an earth mover distance method to detect false data injection attacks in smart grids. The proposed method is built on the dynamic correlation of measurement data between adjacent moments. Firstly, a joint-image-transformation-based scheme is proposed to preprocess the measurement data variation, so that the distribution characteristics of measurement data variation are more significant. Secondly, the deviation between the probability distribution of measurement data variation and the histogram are obtained based on the earth’s mover distance. Finally, a reasonable detection threshold is selected to judge whether there are false data injection attacks. The proposed method is tested using IEEE 14 bus system considering the state variable attacks on different nodes. The results verified that the proposed method has a high detection accuracy against false data injection attacks.

Suggested Citation

  • Zhengwei Qu & Jingchuan Yang & Yansheng Lang & Yunjing Wang & Xiaoming Han & Xinyue Guo, 2022. "Earth-Mover-Distance-Based Detection of False Data Injection Attacks in Smart Grids," Energies, MDPI, vol. 15(5), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:5:p:1733-:d:758755
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    References listed on IDEAS

    as
    1. Dai Wang & Xiaohong Guan & Ting Liu & Yun Gu & Chao Shen & Zhanbo Xu, 2014. "Extended Distributed State Estimation: A Detection Method against Tolerable False Data Injection Attacks in Smart Grids," Energies, MDPI, vol. 7(3), pages 1-22, March.
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    Cited by:

    1. Shunli Li & Linzhang Lu & Qilong Liu & Zhen Chen, 2023. "Graph-Regularized, Sparsity-Constrained Non-Negative Matrix Factorization with Earth Mover’s Distance Metric," Mathematics, MDPI, vol. 11(8), pages 1-14, April.

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