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@article{209166,
        author = {Surya Prakash Chaturvedula and Dr. Pawan Kumar Pareek},
        title = {Ground-Truth Physics Residuals for Detecting Stealthy False Data Injection Attacks in Smart Grids},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {5},
        pages = {975-984},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=209166},
        abstract = {Stealthy false data injection (FDI) attacks exploit a fundamental weakness of conventional residual-based bad-data detection: a carefully structured measurement perturbation can remain statistically acceptable to the state-estimation residual while moving the reported measurements away from the electrical state represented by the actual network. This paper develops and empirically evaluates a ground-truth physics-residual methodology for identifying that discrepancy. The key idea is to compute electrical-consistency residuals directly from the defender-side network admittance matrix (Ybus), reconstructed voltage phasors, reported active/reactive power, and line-flow balance, and to use these residuals as an explicit feature stream in a spatial-temporal detector. The evaluation uses a corrected benchmark of 2,016 simulation runs spanning IEEE 39-bus and IEEE 118-bus systems, 18 balanced scenario cells, and three attacker-knowledge tiers for stealthy FDI. The primary detector combines graph attention, temporal LSTM representation, and the physics-residual stream. On the held-out corrected test split, pooled precision, recall, F1 and ROC-AUC are 0.727, 0.715, 0.721 and 0.815, respectively, compared with F1/ROC-AUC of 0.596/0.569 for a Chi-squared bad-data detector and 0.621/0.677 for a vanilla LSTM. FDI-specific F1 is 0.910, 0.902 and 0.892 for attacker tiers 1–3. Removing the physics stream reduces F1 by 34.8, 38.2 and 36.0 percentage points for the three FDI tiers. A sequence-position control achieves F1=0.0 and ROC-AUC=0.5 after correction of an earlier fixed-onset leakage defect. The results provide strong first-pass evidence that explicit electrical-consistency information materially improves stealthy-FDI detection, while also exposing an important topology-dependent false-positive problem: 0% on IEEE 39-bus versus 25.5% on IEEE 118-bus. The paper therefore presents the residual methodology as a reproducible research mechanism rather than as evidence of deployment readiness.},
        keywords = {smart-grid cybersecurity; false data injection; physics residual; Ybus; bad-data detection; physics-informed machine learning; graph attention network; LSTM; cyber-physical security; power-system state estimation.},
        month = {October},
        }