  In this study, we introduce a novel unsupervised countermeasure for smart
grid power systems, based on generative adversarial networks (GANs). Given the
pivotal role of smart grid systems (SGSs) in urban life, their security is of
particular importance. In recent years, however, advances in the field of
machine learning, have raised concerns about cyber attacks on these systems.
Power systems, among the most important components of urban infrastructure,
have, for example, been widely attacked by adversaries. Attackers disrupt power
systems using false data injection attacks (FDIA), resulting in a breach of
availability, integrity, or confidential principles of the system. Our model
simulates possible attacks on power systems using multiple generators in a
step-by-step interaction with a discriminator in the training phase. As a
consequence, our system is robust to unseen attacks. Moreover, the proposed
model considerably reduces the well-known mode collapse problem of GAN-based
models. Our method is general and it can be potentially employed in a wide
range of one of one-class classification tasks. The proposed model has low
computational complexity and outperforms baseline systems about 14% and 41% in
terms of accuracy on the highly imbalanced publicly available industrial
control system (ICS) cyber attack power system dataset.
