Step-Gan: A One-Class Anomaly Detection Model With Applications To Power System Security

Smart grid systems (SGSs), and in particular power systems, play a vital role in today's urban life. The security of these grids is now threatened by adversaries that use false data injection (FDI) to produce a breach of availability, integrity, or confidential principles of the system. We propose a novel structure for the multi-generator generative adversarial network (GAN) to address the challenges of detecting adversarial attacks. We modify the GAN objective function and the training procedure for the malicious anomaly detection task. The model only requires normal operation data to be trained, making it cheaper to deploy and robust against unseen attacks. Moreover, the model operates on the raw input data, eliminating the need for feature extraction. We show that the model reduces the well-known mode collapse problem of GAN-based systems, it has low computational complexity and considerably outperforms baseline systems about 55% (OCAN) in terms of accuracy on a freely available cyber attack dataset.