The increasing digitalization of smart grids has made them vulnerable to false data injection attacks (FDIAs), which can bypass traditional bad data detection (BDD) schemes and compromise grid security. While machine learning offers promising detection capabilities, existing methods often struggle with generalization, interpretability, and the effective integration of the grid’s inherent spatio-temporal properties. To address these challenges, this paper presents a hierarchical spatio-temporal graph attention network (HST-GAT) for FDIA detection in smart grids. The proposed FDIA detection method employs a dedicated two-stage architecture. First, a graph attention network (GAT) explicitly captures the complex spatial dependencies and physical constraints of the grid topology. Second, a temporal module with multi-head self-attention and a gated recurrent unit (GRU) analyzes evolving attack patterns across time steps. This hierarchical separation ensures a more interpretable and physically grounded representation of cyber intrusions compared to joint spatio-temporal models. Explainability analysis using the SHapley Additive exPlanations (SHAP) method reveals the decision-making process of the proposed FDIA detection method, validating its alignment with the grid topology and identifying the key buses that influence its predictions. The results confirm the robustness of the proposed method and its value in improving cybersecurity in modern smart grids.
Zhang et al. (Sun,) studied this question.