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Intelligent attackers can suitably tamper sensor/actuator data at variousSmart grid surfaces causing intentional power oscillations, which if leftundetected, can lead to voltage disruptions. We develop a novel combination offormal methods and machine learning tools that learns power system dynamicswith the objective of generating unsafe yet stealthy false data based attacksequences. We enable the grid with anomaly detectors in a generalized manner sothat it is difficult for an attacker to remain undetected. Our methodology,when applied on an IEEE 14 bus power grid model, uncovers stealthy attackvectors even in presence of such detectors.

A Learning Assisted Method for Uncovering Power Grid Generation and Distribution System Vulnerabilities