Unifying temporal continuity and structural decomposition for stealthy FDIA detection in smart grids via implicit neural representations
Year: 2026
Type: article
Abstract: False data injection attacks (FDIAs) pose significant threats to power system security by stealthily manipulating measurements to bypass traditional residual-based detectors. While recent time-series detection methods improve robustness by modeling temporal continuity, they often rely on labeled attack data or static architectures with limited adaptability. This paper proposes a fully unsupervised FDIA detection framework based on implicit neural representations (INRs), which model multivariate power system measurements as continuous functions of time. A transformer-based hypernetwork dynamically generates INR parameters, enabling the model to adapt to varying operating conditions without retraining. To unify temporal continuity and structural decomposition, the proposed framework couples continuous-time INR modeling with a hierarchical trend-seasonal-residual representation and a measurement-type-aware residual structure. Anomalies are identified via a weighted reconstruction error using type-normalized anomaly scores. Extensive experiments on IEEE 14-bus and 118-bus test systems demonstrate that the proposed method achieves near-perfect detection accuracy against both optimization-based and controllable FDIAs, significantly outperforming existing supervised, semi-supervised, and unsupervised baselines. Moreover, the INR model exhibits strong sensitivity to subtle, temporally-localized perturbations and remains robust under different attack amplitudes. To the best of our knowledge, this is the first FDIA detection framework leveraging INR-based continuous-time modeling with transformer-driven parameterization, offering a scalable, interpretable, and topology-agnostic solution for securing modern power grids. • Introduces the first INR-based FDIA detection framework for power system measurements. • Embeds a learnable trend-seasonal-residual decomposition within the INR to provide multi-scale inductive bias and enhance interpretability. • Demonstrates near-perfect detection accuracy on IEEE benchmark systems under both optimization-based and controllable FDIAs in a fully unsupervised and topology-agnostic setting.
Authors: Siliang Zhao, Wuman Luo, Qin Shu, Fangwei Xu
Institutions: Sichuan University, Macao Polytechnic University
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,920
OpenAlex ID: W7119016170