  Vulnerability of various machine learning methods to adversarial examples has
been recently explored in the literature. Power systems which use these
vulnerable methods face a huge threat against adversarial examples. To this
end, we first propose a signal-specific method and a universal signal-agnostic
method to attack power systems using generated adversarial examples. Black-box
attacks based on transferable characteristics and the above two methods are
also proposed and evaluated. We then adopt adversarial training to defend
systems against adversarial attacks. Experimental analyses demonstrate that our
signal-specific attack method provides less perturbation compared to the FGSM
(Fast Gradient Sign Method), and our signal-agnostic attack method can generate
perturbations fooling most natural signals with high probability. What's more,
the attack method based on the universal signal-agnostic algorithm has a higher
transfer rate of black-box attacks than the attack method based on the
signal-specific algorithm. In addition, the results show that the proposed
adversarial training improves robustness of power systems to adversarial
examples.

Cryptography and Security;Artificial Intelligence;Machine Learning