Load Redistribution Attack Detection using Machine Learning: A Data-Driven Approach

Three detection techniques are presented against a wide class of cyber-attacks that maliciously redistribute loads by modifying measurements. The detectors use different anomaly detection algorithms based on machine learning techniques: nearest neighbor method, support vector machines, and replicator neural networks. The detectors are tested using a data-driven approach on a realistic dataset comprised of real historical load data in the form of publicly available PJM zonal data mapped to the IEEE 30-bus system. The results show all three detectors to be very accurate, with the nearest neighbor algorithm being the most computational efficient.