AI-based approach to identify compromised meters in data integrity attacks on smart grid

Abstract

False data injection attacks can pose serious threats to the operation and control of power grid. The smarter the power grid gets, the more vulnerable it becomes to cyber-attacks. Various detection methods of cyber-attacks have been proposed in the literature in recent past. However, to completely alleviate the possibility of cyber-threats, the compromised meters must be identified and secured. In this study, the authors are presenting an artificial intelligence (AI)-based identification method to correctly single out the malicious meters. The proposed AI-based method successfully identifies the compromised meters by anticipating the correct measurements in the event of the cyber-attack. New York Independent System Operator load data is mapped with the IEEE 14-bus system to validate the proposed method. The efficiency of the proposed method is compared for artificial neural network and extreme learning machine-based AI techniques. It is observed that both the techniques identify the corrupted meters with high accuracy.