Information-Theoretic Attacks in the Smart Grid

TL;DR

This paper introduces an information-theoretic approach to designing Gaussian random attacks on the smart grid, optimizing for minimal information leakage and detection probability through an optimization framework.

Contribution

It formulates a novel attack construction as an optimization problem balancing information minimization and detection risk, including bounds with imperfect knowledge.

Findings

Attack performance evaluated on IEEE 30-Bus system

Lower bounds established for utility with imperfect knowledge

Numerical results demonstrate attack effectiveness

Abstract

Gaussian random attacks that jointly minimize the amount of information obtained by the operator from the grid and the probability of attack detection are presented. The construction of the attack is posed as an optimization problem with a utility function that captures two effects: firstly, minimizing the mutual information between the measurements and the state variables; secondly, minimizing the probability of attack detection via the Kullback-Leibler divergence between the distribution of the measurements with an attack and the distribution of the measurements without an attack. Additionally, a lower bound on the utility function achieved by the attacks constructed with imperfect knowledge of the second order statistics of the state variables is obtained. The performance of the attack construction using the sample covariance matrix of the state variables is numerically evaluated. The above results are tested in the IEEE 30-Bus test system.