Towards a Unified Resilience Analysis: State Estimation against Integrity Attacks

TL;DR

This paper establishes tight, generic conditions for resilient state estimation under integrity attacks using convex optimization, validated through simulations on power system data.

Contribution

It provides the first comprehensive, tight necessary and sufficient conditions for resilience applicable to a broad class of convex estimators in attack scenarios.

Findings

Conditions are tight with a trivial gap.

Specialized results for scalar measurements.

Experimental validation on IEEE 14-bus system.

Abstract

We consider the problem of resilient state estimation in the presence of integrity attacks. There are m sensors monitoring the state and p of them are under attack. The sensory data collected by the compromised sensors can be manipulated arbitrarily by the attacker. The classical estimators such as the least squares estimator may not provide a reliable estimate under the so-called (p,m)-sparse attack. In this work, we are not restricting our efforts in studying whether any specific estimator is resilient to the attack or not, but instead we aim to present the generic sufficient and necessary conditions for resilience by considering a general class of convex optimization based estimators. The sufficient and necessary conditions are shown to be tight, with a trivial gap. We further specialize our result to scalar sensor measurements case and present some conservative but verifiable results for practical use. Experimental simulations tested on the IEEE 14-bus test system validate the theoretical analysis.