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

TL;DR

This paper introduces extsc{GenAI-FDIA}, a framework benchmarking physics-informed generative models for false data injection attacks in power systems, addressing data scarcity and model fidelity issues.

Contribution

It evaluates diverse physics-compliant generative architectures for attack synthesis, identifies failure modes, and proposes solutions to enhance attack stealthiness and model robustness.

Findings

All architectures achieved evasion rates of ≥86.6% on the 14-bus network.

Applying affine physics projections in normalized space critically reduces attack evasion.

A novel inference-time harmoniser restores full attack stealthiness without retraining.

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 \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. Our empirical results verify that these models generate high-fidelity attacks, with all architectures achieving evasion rates of $\epsilon_{\text{BDD}} \ge 86.6\%$ 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 ($\epsilon_{\text{BDD}}{=}100\%$) 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 ($\kappa \to 0.785$, $\Delta\text{MMD}={-}3.1\%$). Ultimately, \textsc{GenAI-FDIA} delivers a robust recovery blueprint applicable to any physics-constrained generative model deployed for power-system security.