Detection of False Data Injection Attacks on Smart Grids Based on A-BiTG Approach

Abstract

A false data injection attack (FDIA) is the main attack method that threatens the security of smart grids. FDIAs mislead the control center to make wrong judgments by modifying the measurement data of the power grid system. Therefore, the effective and accurate detection of FDIAs is crucial for the safe operation of smart grids. However, the current deep learning-based methods do not fully exploit the short-term local characteristics and long-term dependencies of power grid data and have poor correlation with past and future time series information, resulting in a lack of credibility in the detection results. In view of this, an FDIA detection model combining a bidirectional temporal convolutional network and bidirectional gated recurrent unit with an attention mechanism (A-BiTG) was proposed. The proposed model utilizes a bidirectional time convolutional network (BiTCN) and bidirectional gated recurrent unit (BiGRU) to consider past and future temporal information in the grid. This enhances the ability of the model to capture long-term dependencies and extract features, while also solving the model’s problem of exploding and vanishing gradients. In addition, an attention mechanism (AM) was added to dynamically assign weights to the extracted feature information and retain the most valuable features to improve the detection accuracy of the model. Finally, the proposed method was compared with existing methods on the IEEE 14-bus and IEEE 118-bus test systems. The results show that the proposed detection model is more robust and superior under different noise environments and FDIA signals with different intensities.