Time Series Analysis Neural Networks for Detecting False Data Injection Attacks of Different Rates on Power Grid State Estimation

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False Data Injection Attacks (FDIAs) that target the state estimation pose an immense threat to the security of power grids. Deep Neural Network (DNN) based methods have shown promising results in detecting such FDIAs. Among existing state-of-the-art DNN models, time series analysis DNNs have demonstrated superior FDIA detection capability. This paper discusses the challenges associated with applying time series analysis DNNs for detecting FDIAs and emphasizes the impact of the attack rate on the detection rate of attacks. We demonstrate that existing time series analysis DNNs are highly vulnerable to FDIAs executed at low attack rates. This paper presents various alternative implementations for time series classifiers and time series predictors to improve the FDIA detection rate. A novel method is proposed to train time series classification neural networks to detect FDIAs of any attack rate with high efficiency. Subsequently, an enhanced FDIA detection framework that includes a time series classifier and multiple predictors is presented. Furthermore, an analytical criterion is derived to estimate the FDIA detection rate of time series analysis DNNs under any attack rate. Experimental results obtained on IEEE bus systems using state-of-the-art DNN architectures support the effectiveness of the proposed training method and the proposed framework. The proposed training method significantly improved the detection rate of FDIAs at low attack rates. Up to a 48% improvement in the FDIA detection rate was observed in the proposed framework when compared to the state-of-the-art.