GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks

arXiv:2605.18873v1 Announce Type: cross Abstract: 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 extsc{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. Our empirical results verify that these models generate high-fidelity attacks, with all architectures achieving evasion rates of epsilon_{ext{BDD}} ge 86.6% on the 14-bus network; additionally, limiting an attacker's topological knowledge induces a measurable degradation in stealthiness (p le 0.0022). Crucially, we identify a previously unreported failure mode: applying affine physics projections directly in normalised feature spaces critically displaces the attack vector, collapsing BDD evasion from {sim}55% to <!2% on the 30-bus testbed. We resolve this via a novel inference-time harmoniser, restoring full stealthiness (epsilon_{ext{BDD}}{=}100%) across all physics-informed variants without retraining. Finally, we isolate a covariance-collapse phenomenon (kappa approx {-}0.076) within advanced hybrid architectures and rectify it through 50-epoch warm-up schedules (kappa o 0.785, Deltaext{MMD}={-}3.1%). Ultimately, extsc{GenAI-FDIA} delivers a robust recovery blueprint applicable to any physics-constrained generative model deployed for power-system security.

Source: arXiv cs.AI | 2026-05-20