Limits of Residual-Based Detection for Physically Consistent False Data Injection
Chenhan Xiao, Yang Weng

TL;DR
This paper demonstrates fundamental limitations of residual-based detection methods for false data injection attacks in AC power systems, showing they can be bypassed when manipulated measurements remain physically consistent.
Contribution
It reveals that residual-based detectors can fail under certain physically consistent attack scenarios and introduces a data-driven method to illustrate this vulnerability.
Findings
Residual-based detectors can be bypassed by physically consistent attacks.
Detection failure occurs when manipulated data remains on the measurement manifold.
Fundamental limits of residual-based detection are characterized in AC power systems.
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…
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Taxonomy
TopicsSmart Grid Security and Resilience · Power System Optimization and Stability · Power Systems Fault Detection
