A Deep and Scalable Unsupervised Machine Learning System for Cyber-Attack Detection in Large-Scale Smart Grids

• Published in: IEEE access Vol. 7; pp. 80778 - 80788
• Main Authors: Karimipour, Hadis, Dehghantanha, Ali, Parizi, Reza M., Choo, Kim-Kwang Raymond, Leung, Henry
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Anomalies; Anomaly; Correlation analysis; cyber-attack; Cybersecurity; Electric power distribution; Feature extraction; Generators; Machine learning; Power system reliability; Reliability; Security; Smart grid; Smart grid technology; Smart grids; Software reliability; Statistical correlation; statistical property; Subsystems; Unsupervised learning
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2019.2920326

Smart grid technology increases reliability, security, and efficiency of the electrical grids. However, its strong dependencies on digital communication technology bring up new vulnerabilities that need to be considered for efficient and reliable power distribution. In this paper, an unsupervised anomaly detection based on statistical correlation between measurements is proposed. The goal is to design a scalable anomaly detection engine suitable for large-scale smart grids, which can differentiate an actual fault from a disturbance and an intelligent cyber-attack. The proposed method applies feature extraction utilizing symbolic dynamic filtering (SDF) to reduce computational burden while discovering causal interactions between the subsystems. The simulation results on IEEE 39, 118, and 2848 bus systems verify the performance of the proposed method under different operation conditions. The results show an accuracy of 99%, true positive rate of 98%, and false positive rate of less than 2%