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

Computer Science > Cryptography and Security [Submitted on 15 May 2026] GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks Mohammad A. Razzaque, Muta Tah Hira 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. Comments: Submitted to IEEE Transactions on Smart Grid Subjects: Cryptography and Security (cs.CR); Artificial Intelligence (cs.AI); Machine Learning (cs.LG) Cite as: arXiv:2605.18873 [cs.CR]   (or arXiv:2605.18873v1 [cs.CR] for this version)   https://doi.org/10.48550/arXiv.2605.18873 Focus to learn more Submission history From: Mohammad Abdur Razzaque Dr. [view email] [v1] Fri, 15 May 2026 19:51:08 UTC (1,111 KB) Access Paper: HTML (experimental) view license Current browse context: cs.CR < prev   |   next > new | recent | 2026-05 Change to browse by: cs cs.AI cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar Export BibTeX Citation Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Demos Related Papers About arXivLabs Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)