Limits of Residual-Based Detection for Physically Consistent False Data Injection

2026-02-10

AI Summary

This work addresses the fundamental limitation of conventional residual-based detection methods in identifying physically consistent false data injection attacks (FDIAs) in AC power systems, as these methods fail to detect perturbations lying on the manifold defined by AC power flow equations and measurement redundancy. For the first time, the study rigorously characterizes this intrinsic vulnerability from the perspective of the measurement manifold’s geometry. To circumvent reliance on an exact system model, the authors propose a data-driven approach that leverages the universal functional structure of AC power flows to generate physically plausible attack vectors. Extensive experiments on multiple standard test systems demonstrate that such attacks can effectively evade detection, revealing the specific conditions and patterns under which residual-based mechanisms fail, thereby exposing the inherent weaknesses of current detection frameworks.

Technology Category

• Computer Vision: Adversarial Attacks & Robustness
• Constraint Satisfaction and Optimization: Constraint Learning and Acquisition
• Machine Learning: Adversarial Learning & Robustness

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Large-scale security measurements
• Responsible Web: Measurement, analysis, and circumvention of Web censorship

Abstract

False data injection attacks (FDIAs) pose a persistent challenge to AC power system state estimation. In current practice, detection relies primarily on topology-aware residual-based tests that assume malicious measurements can be distinguished from normal operation through physical inconsistency reflected in abnormal residual behavior. This paper shows that this assumption does not always hold: when FDIA scenarios produce manipulated measurements that remain on the measurement manifold induced by AC power flow relations and measurement redundancy, residual-based detectors may fail to distinguish them from nominal data. The resulting detectability limitation is a property of the measurement manifold itself and does not depend on the attacker's detailed knowledge of the physical system model. To make this limitation observable in practice, we present a data-driven constructive mechanism that incorporates the generic functional structure of AC power flow to generate physically consistent, manifold-constrained perturbations, providing a concrete witness of how residual-based detectors can be bypassed. Numerical studies on multiple AC test systems characterize the conditions under which detection becomes challenging and illustrate its failure modes. The results highlight fundamental limits of residual-based detection in AC state estimation and motivate the need for complementary defenses beyond measurement consistency tests.

Problem

• False Data Injection Attacks
• State Estimation
• Residual-Based Detection
• Measurement Manifold
• AC Power Systems

Innovation

• false data injection attack
• measurement manifold
• residual-based detection
• AC state estimation
• data-driven construction