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TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features

Author

Listed:
  • Pengyi Liao

    (Department of Electrical and Computer Engineering (ECE), Concordia University, Montréal, QC H3G 1M8, Canada)

  • Jun Yan

    (Concordia Institute for Information Systems Engineering (CIISE), Concordia University, Montréal, QC H3G 1M8, Canada)

  • Jean Michel Sellier

    (Ericsson GAIA Montréal, AI hub Canada, Montréal, QC H4S 0B6, Canada)

  • Yongxuan Zhang

    (Department of Computer Science and Software Engineering (CSSE), Concordia University, Montréal, QC H3G 1M8, Canada)

Abstract

For attack detection in the smart grid, transfer learning is a promising solution to tackle data distribution divergence and maintain performance when facing system and attack variations. However, there are still two challenges when introducing transfer learning into intrusion detection: when to apply transfer learning and how to extract effective features during transfer learning. To address these two challenges, this paper proposes a transferability analysis and domain-adversarial training (TADA) framework. The framework first leverages various data distribution divergence metrics to predict the accuracy drop of a trained model and decides whether one should trigger transfer learning to retain performance. Then, a domain-adversarial training model with CNN and LSTM is developed to extract the spatiotemporal domain-invariant features to reduce distribution divergence and improve detection performance. The TADA framework is evaluated in extensive experiments where false data injection (FDI) attacks are injected at different times and locations. Experiments results show that the framework has high accuracy in accuracy drop prediction, with an RMSE lower than 1.79%. Compared to the state-of-the-art models, TADA demonstrates the highest detection accuracy, achieving an average accuracy of 95.58%. Moreover, the robustness of the framework is validated under different attack data percentages, with an average F1-score of 92.02%.

Suggested Citation

  • Pengyi Liao & Jun Yan & Jean Michel Sellier & Yongxuan Zhang, 2022. "TADA: A Transferable Domain-Adversarial Training for Smart Grid Intrusion Detection Based on Ensemble Divergence Metrics and Spatiotemporal Features," Energies, MDPI, vol. 15(23), pages 1-18, November.
  • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8778-:d:979986
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    References listed on IDEAS

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    1. Kamran Shaukat & Suhuai Luo & Vijay Varadharajan & Ibrahim A. Hameed & Shan Chen & Dongxi Liu & Jiaming Li, 2020. "Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity," Energies, MDPI, vol. 13(10), pages 1-27, May.
    2. Sarra Houidi & Dominique Fourer & François Auger & Houda Ben Attia Sethom & Laurence Miègeville, 2021. "Comparative Evaluation of Non-Intrusive Load Monitoring Methods Using Relevant Features and Transfer Learning," Energies, MDPI, vol. 14(9), pages 1-28, May.
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