# Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids Source: [Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids]() Collection index: [llms.txt]() Data updated: 2026-06-20T05:00:13.953Z Authors: Farhin Farhad Riya, Shahinul Hoque, Yingyuan Yang, Jinyuan Sun, Kevin Tomsovic Published: 2026-06-18T16:01:39.000Z ## Abstract Deep Neural Networks DNNs have achieved remarkable accuracy in various tasks including their application in CyberPhysical Systems CPS for detecting False Data Injection Attacks FDIA during critical operations However the unique infrastructure of CPS makes DNNs vulnerable to exploitation by attackers aiming to evade detection Additionally the distinct nature of CPS presents challenges for conventional defense mechanisms against FDIA This paper proposes an innovative defense framework that strengthens DNNs against such attacks by introducing an additional input layer that performs padding in the input samples using pseudofeature values derived from the inputs statistical distribution This padding increases the input dimensionality in a randomized and dataaware manner making adversarial attacks computationally infeasible due to the nontransferable nature of crafted perturbations and the unpredictability of the padded structure Our method is lightweight modelagnostic and requires no modifications to the core architecture making it highly deployable in realworld CPS settings We evaluated our framework on critical power grid applications such as state estimation using the IEEE 14bus 30bus 118bus and 300bus systems Experiments under adversarial settings demonstrate that our padding strategy significantly improves model robustness with negligible impact on performance and effectively mitigates attacks that would otherwise bypass conventional defenses ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Attack Detection on IEEE 14-bus system](): Attack Detection; IEEE 14-bus system; Accuracy: 94.2. - [Attack Detection on IEEE 300-bus system](): Attack Detection; IEEE 300-bus system; Accuracy: 96.1. - [Attack Detection on IEEE 30-bus system](): Attack Detection; IEEE 30-bus system; Accuracy: 92. - [Attack Detection on IEEE 118-bus system](): Attack Detection; IEEE 118-bus System; Accuracy: 93.4. ## Original sources and implementation [Original paper]() [PDF]()