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<p>This paper addresses the problem of detecting false data injection attacks (FDI) for state estimation with non-Gaussian noises. During the estimation process, potential attacks and non-Gaussian noise can lead to the non-Gaussian property of the innovation, rendering traditional attack detection methods ineffective. To tackle this issue, we propose a novel detection strategy using Kullback–Leibler (KL) divergence as a detection metric, which adapts well to non-Gaussian scenarios. Furthermore, we adopt a Q-learning strategy to train the safety threshold of the detector to improve the reliability of detection. Through verification via Python simulation experiments, we demonstrate that the designed detector has a negligible impact on estimation performance, and provide an effective detection performances against FDI attacks.</p>

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Q-Learning Based Detector Design for State Estimation Under Non-gaussian Noises

  • 期刊论文

  • 发表日期：

    2025年1月31日

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  • Yue Luo,
  • Yun Liu,
  • Wen Yang,
  • Xiaofan Wang

摘要

This paper addresses the problem of detecting false data injection attacks (FDI) for state estimation with non-Gaussian noises. During the estimation process, potential attacks and non-Gaussian noise can lead to the non-Gaussian property of the innovation, rendering traditional attack detection methods ineffective. To tackle this issue, we propose a novel detection strategy using Kullback–Leibler (KL) divergence as a detection metric, which adapts well to non-Gaussian scenarios. Furthermore, we adopt a Q-learning strategy to train the safety threshold of the detector to improve the reliability of detection. Through verification via Python simulation experiments, we demonstrate that the designed detector has a negligible impact on estimation performance, and provide an effective detection performances against FDI attacks.