Pseudo-Feature Padding: A Lightweight Defense Against False Data Injection in Power Grids

2026-06-18

AI Summary

This work addresses the vulnerability of deep neural networks in power cyber-physical systems to false data injection attacks, a challenge inadequately mitigated by conventional defense mechanisms due to architectural incompatibility. The authors propose a lightweight, model-agnostic defense framework that enhances input data with statistically generated pseudo-features through random high-dimensional padding, requiring no modification to the original model architecture. By leveraging data-aware dimensional expansion, the approach renders adversarial perturbations non-transferable and computationally infeasible. Evaluated on IEEE 14-, 30-, 118-, and 300-bus systems, the method significantly improves the robustness of state estimation models against sophisticated attacks capable of bypassing traditional defenses, while imposing negligible overhead on normal operational performance.

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

Problem

• False Data Injection Attack
• Cyber-Physical Systems
• Deep Neural Networks
• Adversarial Attacks
• Power Grids

Innovation

• Pseudo-Feature Padding
• False Data Injection Attack
• Cyber-Physical Systems
• Adversarial Robustness
• Lightweight Defense