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Semi-asynchronous federated learning-based privacy-preserving intrusion detection for advanced metering infrastructure

Author

Listed:
  • Xia, Zhuoqun
  • Zhou, Hongmei
  • Hu, Zhenzhen
  • Jiang, Qisheng
  • Zhou, Kaixin

Abstract

The emergence of smart grid brings great convenience to users and power companies, but also brings many new problems, among which the most prominent one is network attack security. Although federated learning works well in dealing with smart grid network attacks, it suffers from gradient leakage, client node failure and a single type of training model. Therefore, this paper proposes a semi-asynchronous federated learning-based privacy-preserving intrusion detection for advanced metering infrastructure (AMI). First, we design a hierarchical federated learning framework based on chained secure multiparty computing, which allows concentrators to collaboratively train models to protect local gradients. Second, we adapt the framework to the AMI network structure characteristics, and design a semi-asynchronous model distribution protocol. Finally, we build an ensemble model based on temporal convolutional network and gated recurrent unit (TCN-GRU) to detect AMI network attacks. The experimental results show that the proposed method can achieve 99.23% accuracy than existing methods.

Suggested Citation

  • Xia, Zhuoqun & Zhou, Hongmei & Hu, Zhenzhen & Jiang, Qisheng & Zhou, Kaixin, 2025. "Semi-asynchronous federated learning-based privacy-preserving intrusion detection for advanced metering infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 49(C).
    • Handle: RePEc:eee:ijocip:v:49:y:2025:i:c:s1874548225000046
    DOI: 10.1016/j.ijcip.2025.100742
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    References listed on IDEAS

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    1. Hansen, Aaron & Staggs, Jason & Shenoi, Sujeet, 2017. "Security analysis of an advanced metering infrastructure," International Journal of Critical Infrastructure Protection, Elsevier, vol. 18(C), pages 3-19.
    2. Ke Zhang & Zhi Hu & Yufei Zhan & Xiaofen Wang & Keyi Guo, 2020. "A Smart Grid AMI Intrusion Detection Strategy Based on Extreme Learning Machine," Energies, MDPI, vol. 13(18), pages 1-19, September.
    3. Guojie Liu & Jianbiao Zhang, 2020. "CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-11, May.
    4. Yuancheng Li & Rixuan Qiu & Sitong Jing, 2018. "Intrusion detection system using Online Sequence Extreme Learning Machine (OS-ELM) in advanced metering infrastructure of smart grid," PLOS ONE, Public Library of Science, vol. 13(2), pages 1-16, February.
    5. Kabalci, Yasin, 2016. "A survey on smart metering and smart grid communication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 302-318.
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