  Information theoretic sparse attacks that minimize simultaneously the
information obtained by the operator and the probability of detection are
studied in a Bayesian state estimation setting. The attack construction is
formulated as an optimization problem that aims to minimize the mutual
information between the state variables and the observations while guaranteeing
the stealth of the attack. Stealth is described in terms of the
Kullback-Leibler (KL) divergence between the distributions of the observations
under attack and without attack. To overcome the difficulty posed by the
combinatorial nature of a sparse attack construction, the attack case in which
only one sensor is compromised is analytically solved first. The insight
generated in this case is then used to propose a greedy algorithm that
constructs random sparse attacks. The performance of the proposed attack is
evaluated in the IEEE 30 Bus Test Case.
