Abstract

This paper presents a novel targeted adversarial attack based on the fast gradient sign method on data-driven distribution system state estimation. In this attack, malicious sensor data are crafted such that the state estimator's output moves away from the latent state of the system in a direction specified by the attacker. Through extensive simulation on a test distribution system, we expose the vulnerability of a state-of-the-art data-driven state estimation technique to the proposed adversarial attack. We also show that the bad data detection method that is commonly used to safeguard state estimation is ineffective against this attack, and this can have deleterious effects on a voltage regulation scheme that incorporates state estimates. To address this vulnerability, we analyze the dispersion of perfeature attribution scores and argue that this dispersion can be used be to detect adversarially crafted data. We corroborate the efficacy of this detection method by comparing it with the conventional bad data detection method and two other baselines.