Work

False Data Injection Attack Detection in Smart Grid Based on Learnable Unified Neighborhood-Based Anomaly Ranking

Year: 2025

Type: article

Abstract: To address the detection of stealthy False Data Injection Attacks (FDIA) that evade traditional detection mechanisms in smart grids, this paper proposes an unsupervised learning framework named SHAP-LUNAR (SHapley Additive ExPlanations-Learnable Unified Neighborhood-based Anomaly Ranking). This framework overcomes the limitations of existing methods, including parameter sensitivity, inefficiency in high-dimensional spaces, dependency on labeled data, and poor interpretability. Key contributions include (1) constructing a lightweight k-nearest neighbor graph through learnable graph aggregation to unify local anomaly detection, significantly reducing sensitivity to core parameters; (2) generating negative samples via boundary uniform sampling to eliminate dependency on real attack labels; (3) integrating SHAP for quantifying feature contributions to achieve feature-level model interpretation. Experimental results on IEEE 14-bus and IEEE 118-bus systems demonstrate F1 scores of 99.40% and 96.79%, respectively, outperforming state-of-the-art baselines. The method combines high precision, strong robustness, and interpretability.

Source: Electronics

DOI: 10.3390/electronics14173396