Fengyong Li1,*, Weicheng Shen1, Zhongqin Bi1, Xiangjing Su2
CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2095-2115, 2024, DOI:10.32604/cmes.2023.044431
Abstract False data injection attack (FDIA) is an attack that affects the stability of grid cyber-physical system (GCPS) by evading the detecting mechanism of bad data. Existing FDIA detection methods usually employ complex neural network models to detect FDIA attacks. However, they overlook the fact that FDIA attack samples at public-private network edges are extremely sparse, making it difficult for neural network models to obtain sufficient samples to construct a robust detection model. To address this problem, this paper designs an efficient sample generative adversarial model of FDIA attack in public-private network edge, which can effectively… More >
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