Information-Theoretic Attacks in the Smart Grid
This work addresses the vulnerability of state estimation in smart grids to stealthy attacks that degrade monitoring quality, which is critical for grid operators.
The paper proposes Gaussian random attacks that minimize both the information obtained by the grid operator and the probability of detection, achieving a trade-off quantified via mutual information and Kullback-Leibler divergence. Numerical evaluations on the IEEE 30-Bus system demonstrate the attack's effectiveness.
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.