False data injection attacks detection in smart grids based on TCN prediction model

Wendi Ma; Hong Wen; Wenjing Hou; Ruixiang Yao; Yanxu Zhu; Dibao Yan; Ziang Zhao

doi:10.1117/12.3049252

17 October 2024 False data injection attacks detection in smart grids based on TCN prediction model

Wendi Ma, Hong Wen, Wenjing Hou, Ruixiang Yao, Yanxu Zhu, Dibao Yan, Ziang Zhao

Author Affiliations +

Proceedings Volume 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024); 132890Y (2024) https://doi.org/10.1117/12.3049252
Event: The International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 2024, Hangzhou, China

Abstract

The injection of false data attack (FDIA) poses a serious threat to smart grids, and accurate detection of FDIAs is crucial for the secure and stable operation of the grid. A promising approach for FDIA detection based on edge computing is to adopt a scheme that involves prediction before classification, taking into account the temporal correlations of measurement data. This paper proposes a FDIA detection model based on temporal convolutional network (TCN). The model effectively captures long-term dependencies in sequential data, thereby enhancing data prediction performance. Simulation results demonstrate that TCN exhibits superior detection performance under common neural network architectures, compared to typical recurrent neural network (RNN) models. This improvement facilitates highlighting the differences between injected false data and genuine data during the subsequent classification stage, making anomaly detection algorithms more capable of identifying abnormal situations and thereby enhancing the accuracy of FDIA detection.

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

Citation Download Citation

Wendi Ma, Hong Wen, Wenjing Hou, Ruixiang Yao, Yanxu Zhu, Dibao Yan, and Ziang Zhao "False data injection attacks detection in smart grids based on TCN prediction model", Proc. SPIE 13289, International Conference on Optical Communication and Optoelectronic Technology (OCOT 2024), 132890Y (17 October 2024); https://doi.org/10.1117/12.3049252

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KEYWORDS

Data modeling

Convolution

Neural networks

Power grids

Network architectures

Education and training

Detection and tracking algorithms

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