A Transformer-Based Mixture-of-Experts Framework for False Data Injection Attack Detection and Localization
False data injection attacks (FDIAs) threaten smart-grid operation by manipulating measurement data and misleading power system state estimation. Although recent data-driven methods have shown promising performance, many existing approaches rely on a single graph-filtering mechanism and therefore struggle to adapt to diverse cyberattack patterns. This paper proposes a topology-aware encoder-only transformer mixture-of-experts framework for joint FDIA detection and localization. The proposed model integrates Laplacian positional encoding and diffusion-biased self-attention to capture global spatial dependencies, while a densely routed mixture-of-experts module composed of Chebyshev and autoregressive moving-average graph convolutional experts adaptively applies complementary graph filters based on the current grid state. The framework is evaluated on the IEEE 118- and 300-bus systems using the New York Independent System Operator load profile. Experimental results demonstrate that the proposed approach achieves up to 93.91% detection F1 score and 84.97% localization F1 score, outperforming existing benchmark methods while maintaining low false-alarm rates.