  We propose a generative adversarial network (GAN) based deep learning method
that serves the dual role of both identification and mitigation of
cyber-attacks in wide-area damping control loops of power systems. Two specific
types of attacks considered are false data injection and denial-of-service
(DoS). Unlike existing methods, which are either model-based or model-free and
yet require two separate learning modules for detection and mitigation leading
to longer response times before clearing an attack, our deep learner
incorporate both goals within the same integrated framework. A Long Short-Term
Memory (LSTM) encoder-decoder based GAN is proposed that captures the temporal
dynamics of the power system significantly more accurately than fully-connected
GANs, thereby providing better accuracy and faster response for both goals. The
method is validated using the IEEE 68-bus power system model.
