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

• Published in: IET generation, transmission & distribution transmission & distribution, 2018-03, Vol.12 (5), p.1052-1066
• Main Authors: Khanna, Kush, Panigrahi, Bijaya Ketan, Joshi, Anupam
• Format: Article
• Language: English
• Subjects: artificial intelligence; artificial intelligence identification method; compromised metre identification; cyber‐attacks; cyber‐threats; data integrity attacks; extreme learning machine‐based AI techniques; false data injection attacks; IEEE 14‐bus system; learning (artificial intelligence); power engineering computing; power system security; Research Article; security of data; smart grid; smart power grids
• ISSN: 1751-8687; 1751-8695; 1751-8695
• DOI: 10.1049/iet-gtd.2017.0455

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.