GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
Training and evaluating false data injection attack (FDIA) detectors for power systems is constrained by data scarcity. Operational grid measurements are commercially sensitive, and hand-crafted attacks fail to capture complex distributional structures imposed by network physics. We present \textsc{GenAI-FDIA}, a framework benchmarking a pool of $P{=}20$ architectures for physics-compliant FDIA synthesis, spanning Wasserstein GANs, MMD-VAEs, normalising flows, diffusion models, and cross-family hybrids. These are evaluated across three IEEE testbeds (14-bus DC, 30-bus DC, and 14-bus AC) under a 60/20/20 chronological split using data-driven Bad Data Detection (BDD) threshold calibration. ...
| Authors | Mohammad A. Razzaque, Muta Tah Hira |
|---|---|
| Primary category | cs.CR |
| Published | 15 May 2026 |
| Last updated at source | 15 May 2026 |
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