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Graph-based detection for false data injection attacks in power grid

Author

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  • Li, Xueping
  • Wang, Yaokun
  • Lu, Zhigang

Abstract

False data injection attack (FDIA) is the main network attack type threatening power system. FDIA affect the accuracy of data by modifying the measured values of measuring equipment. When the power grid topology has not changed, the data-driven detection method has high detection accuracy. However, the power grid topology changes when faults, maintenance or adjustment in power flow distribution occur in the power system. The data-driven detection method can not capture the spatial feature changes, and the detection accuracy decreases. Considering the changes of power grid topology, a false data detection method based on graph neural network is proposed in this paper, which extracts the spatial features of power grid topology information and operation data through gated graph neural network (GGNN). To enhance node representation in this method, the attention mechanism is adopted to assign aggregation weights to neighbor nodes. This method is tested on IEEE 14-bus and IEEE 118-bus systems. The experimental simulation results show that compared with other data-driven detection methods, the proposed method can significantly improve the detection accuracy under the changes of power grid topology.

Suggested Citation

  • Li, Xueping & Wang, Yaokun & Lu, Zhigang, 2023. "Graph-based detection for false data injection attacks in power grid," Energy, Elsevier, vol. 263(PC).
    • Handle: RePEc:eee:energy:v:263:y:2023:i:pc:s0360544222027517
    DOI: 10.1016/j.energy.2022.125865
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    References listed on IDEAS

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    1. Lin, Wen-Ting & Chen, Guo & Huang, Yuhan, 2022. "Incentive edge-based federated learning for false data injection attack detection on power grid state estimation: A novel mechanism design approach," Applied Energy, Elsevier, vol. 314(C).
    2. Rodofile, Nicholas R. & Radke, Kenneth & Foo, Ernest, 2019. "Extending the cyber-attack landscape for SCADA-based critical infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 25(C), pages 14-35.
    3. Tan, Sen & Xie, Peilin & Guerrero, Josep M. & Vasquez, Juan C., 2022. "False Data Injection Cyber-Attacks Detection for Multiple DC Microgrid Clusters," Applied Energy, Elsevier, vol. 310(C).
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    Cited by:

    1. Zhu, Tianci & Wang, Jun & Zhu, Yonghai & Chen, Haoran & Zhang, Hang & Yin, Shanshan, 2024. "Power grid network security: A lightweight detection model for composite false data injection attacks using spatiotemporal features," International Journal of Critical Infrastructure Protection, Elsevier, vol. 46(C).
    2. Muhammad Awais Shahid & Fiaz Ahmad & Rehan Nawaz & Saad Ullah Khan & Abdul Wadood & Hani Albalawi, 2023. "A Novel False Measurement Data Detection Mechanism for Smart Grids," Energies, MDPI, vol. 16(18), pages 1-17, September.
    3. Huang, Bin & Wang, Jianhui & Huang, Xiaoge, 2026. "Aligning quantum kernels for detecting false data injection attacks in power systems," Applied Energy, Elsevier, vol. 407(C).

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