Sparse Data Injection Attacks on Smart Grid: An Information-Theoretic Approach

Abstract

Sparse data injection attacks are data injection attacks that target a limited number of sensors in the smart grid paradigm. In this paper, sparse attack construction strategy is proposed for the information-theoretic stealth attacks, in which the attacker minimizes the amount of information acquired by the operator about the power system from the observations and the probability of being detected simultaneously. For Gaussian distributed state variables, closed-form expression is proposed for the optimal Gaussian sparse attacks when the attacker targets deterministic observations and gets access to the full system information. Based on that, two target observation selection strategies are introduced to find the vulnerable observations in the system, in which the algorithms take different tradeoff strategies between performance and computational time. Furthermore, a locally perfect stealthy attack construction strategy is proposed for the case in which the attacker only has partial system information but gets access to real-time realizations of the local state variables. The optimality of Gaussian sparse attacks and the performance of the selection strategies are validated on the IEEE 30 Bus and 118 Bus test systems.