Power grid false data injection attack detection method based on S transform and LSTM

Bin Zhao; Yi Li; Zhibo Zhang; Libo Zhong; Yaopeng Han; Yifan Yang

doi:10.1117/12.3034843

7 August 2024 Power grid false data injection attack detection method based on S transform and LSTM

Bin Zhao, Yi Li, Zhibo Zhang, Libo Zhong, Yaopeng Han, Yifan Yang

Proceedings Volume 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024); 132240R (2024) https://doi.org/10.1117/12.3034843
Event: 4th International Conference on Internet of Things and Smart City, 2024, Hangzhou, China

Abstract

As the power system gradually moves towards a new energy Internet, large-scale sensing measurements are deployed in the power system. This provides data support for data-driven false data injection attack detection methods. In order to solve the problem of low classification and recognition accuracy of False Data Injection Attacks (FDIAs), this paper proposes a power grid FDIAs detection method based on S Transform (ST) and Long Short-Term Memory (LSTM) network. This method uses discrete ST to perform time-frequency analysis on the power grid measurement signal. By extracting time-frequency features of power measurement data, false features are highlighted. Then, the LSTM network is combined to accurately classify FDIAs. Simulation experiments show that the attack detection accuracy of this method reaches more than 95%, and the false alarm rate is less than 5%.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Bin Zhao, Yi Li, Zhibo Zhang, Libo Zhong, Yaopeng Han, and Yifan Yang "Power grid false data injection attack detection method based on S transform and LSTM", Proc. SPIE 13224, 4th International Conference on Internet of Things and Smart City (IoTSC 2024), 132240R (7 August 2024); https://doi.org/10.1117/12.3034843

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