Graph-Based Assessment of Vulnerability to False Data Injection Attacks in Distribution Networks

Abstract

A false data injection attack (FDIA) targeting measurements can bypass the distribution system state estimator and negatively affect the control and decision tasks of the distribution management system. A measurement becomes more vulnerable if the attacker has many sparse attack vectors to choose from to attack this measurement without getting detected. A fast graph-based algorithm is proposed in this article to rank all measurements based on FDIA vulnerability in distribution networks. Though the problem of ranking meters based on vulnerability to FDIA is an offline problem, it is combinatorial and the brute force method for solving this problem is not feasible. The proposed algorithm assigns a higher rank to a measurement if it can be attacked by a large number of sparse attack vectors, by leveraging the radial structure of distribution networks and topological properties of power flows. The proposed algorithm has been shown to have applications in identifying meters that need to be secured in order to reduce vulnerability to FDIA, evaluating the impact of pseudo-meters and secured meters on FDIA vulnerability, and determining optimal measurement placement to mitigate FDIA vulnerability.