A Lightweight Framework for Measurement Causality Extraction and FDIA Localization

False Data Injection Attack (FDIA) has become a growing concern in modern cyber-physical power systems. Many learning-based approaches have utilized the statistical correlation patterns between measurements to facilitate the detection and localization of FDIA. However, these correlation patterns are susceptible to the distribution drift of measurement data, which can be induced by changes in system operating points or variations in attack strength, leading to degraded model performance. Causal inference serves as a promising solution to this problem, as it can embed the physical relationship between measurements as causal patterns that are robust against data distribution drifts. However, causal inference is also computationally demanding. To leverage its advantages and address the computational cost issue, this paper proposes a lightweight framework based on causal inference and Graph Attention Networks (GATs) to extract causal patterns between measurements and locate FDIAs. The proposed framework consists of two levels. The lower level uses an X-learner algorithm to estimate the causality strength between measurements and generate Measurement Causality Graphs (MCGs). The upper level then applies a GAT to identify the anomaly patterns in the MCGs. Since the extracted causality patterns are intrinsically related to the measurements, it is easier for the upper level model to identify the attacked nodes than the existing FDIA localization approaches. A physical neighbor masking strategy is implemented to cut down the computational cost of both levels. The performance of the proposed framework is evaluated on the IEEE 39-bus and 118-bus systems. Experimental results show that the causality-based FDIA localization mechanism provides a lightweight solution to interpretable measurement causality extraction and robust FDIA localization.