Dependency-Aware GraphSAGE-Based Interpretable FDIA Detection Using BiLSTM With SE-Attention in Smart Grids

False data injection attacks (FDIAs) refer to attackers exploiting vulnerabilities in the detection of bad data in smart grid energy management systems to maliciously manipulate state estimation results in the cyber–physical system, resulting in unstable operation of the power system. Existing deep learning-based detection schemes often fail to capture the spatial topology features and long-term dependencies in power grid data well. Meanwhile, the complexity of deep detection models makes them a “difficult-to-interpret system,” reducing the credibility of detection results. To address the aforementioned challenges, this article presents a dependency-aware deep interpretable FDIA detection model. The proposed model first introduces graph sample and aggregate (GraphSAGE) network to extract spatial topological features, which are used to represent the deep spatial topological dependencies of adjacent data nodes. Subsequently, we build a Bidirectional Long Short-Term Memory (BiLSTM) network with a Squeeze-and-Excitation (SE) attention module, which can efficiently aggregate attack characteristics and long-term dependency information by dynamically capturing the potential correlations between FDIAs detection and measurement data. Furthermore, the SHapley Additive exPlanations (SHAP) method is used to demonstrate the interpretability of the model in the spatial–temporal dimensions and then provide the basis for high-precision detection results. A series of extensive experiments are carried out over the IEEE 14-bus and 118-bus test systems. The experimental results demonstrate that the proposed model presents a superior overall performance comparing with several state-of-the-art FDIA detection models, and provides reasonable interpretability from the spatial–temporal dimensions.