Stealth Data Injection Attacks with Sparsity Constraints

TL;DR

This paper introduces new sparse stealth attack methods on power systems that minimize mutual information and are designed to be undetectable, demonstrating effective disruption with few compromised sensors.

Contribution

It proposes novel Gaussian-based attack constructions with sparsity constraints and two heuristic algorithms, improving stealth attack effectiveness in power systems.

Findings

Effective attack construction with low sensor compromise

Algorithms outperform baseline in IEEE test systems

Stealth attacks cause significant disruption

Abstract

Sparse stealth attack constructions that minimize the mutual information between the state variables and the observations are proposed. The attack construction is formulated as the design of a multivariate Gaussian distribution that aims to minimize the mutual information while limiting the Kullback-Leibler divergence between the distribution of the observations under attack and the distribution of the observations without attack. The sparsity constraint is incorporated as a support constraint of the attack distribution. Two heuristic greedy algorithms for the attack construction are proposed. The first algorithm assumes that the attack vector consists of independent entries, and therefore, requires no communication between different attacked locations. The second algorithm considers correlation between the attack vector entries which results in better attack performance at the expense of coordination between different locations. We numerically evaluate the performance of the proposed attack constructions on IEEE test systems and show that it is feasible to construct stealth attacks that generate significant disruption with a low number of compromised sensors.