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Extracting Physical Causality from Measurements to Detect and Localize False Data Injection Attacks

False Data Injection Attack (FDIA) has become a growing concern in modern cyber-physical power systems. Most existing FDIA detection techniques project the raw measurement data into a high-dimensional latent space to separate normal and attacked samples. These approaches focus more on the statistical correlations of data values and are therefore susceptible to data distribution drifts induced by changes in system operating points or changes in FDIA types and strengths, especially for FDIA localization tasks. Causal inference, on the other hand, extracts the causality behind the coordinated fluctuations of different measurements. The causality patterns are determined by fundamental physical laws such as Ohm's Law and Kirchhoff's Law. They are sensitive to the violation of physical laws caused by FDIA, but tend to remain stable with the drift of system operating points. Leveraging this advantage, this paper proposes a joint FDIA detection and localization framework based on causal inference and the Graph Attention Network (GAT) to identify the attacked system nodes. The proposed framework consists of two levels. The lower level uses the 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 to figure out the attacked nodes than the existing FDIA localization approaches. The performance of the proposed framework is evaluated on the IEEE 39-bus system. Experimental results show that the causality-based FDIA detection and localization mechanism is highly interpretable and robust.

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