Adversarial Attacks and Defense Methods for Power Quality Recognition

TL;DR

This paper explores vulnerabilities of power quality recognition systems to adversarial attacks, proposing new attack methods and an adversarial training defense to improve system robustness.

Contribution

It introduces signal-specific and universal signal-agnostic attack methods for power systems and demonstrates the effectiveness of adversarial training as a defense.

Findings

Signal-specific attack has less perturbation than FGSM.

Universal signal-agnostic attack achieves higher transfer rates.

Adversarial training enhances system robustness.

Abstract

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.