Adversarial Machine Learning Attack on Modulation Classification

TL;DR

This paper evaluates the robustness of ML-based modulation classifiers against the Carlini & Wagner adversarial attack, revealing their vulnerability and highlighting the need for more secure models in cognitive networks.

Contribution

First to apply the C-W attack to various ML models for modulation classification, demonstrating their lack of robustness against adversarial examples.

Findings

ML classifiers are vulnerable to C-W attack

Current models do not deter adversarial examples

Highlights need for more robust modulation classifiers

Abstract

Modulation classification is an important component of cognitive self-driving networks. Recently many ML-based modulation classification methods have been proposed. We have evaluated the robustness of 9 ML-based modulation classifiers against the powerful Carlini \& Wagner (C-W) attack and showed that the current ML-based modulation classifiers do not provide any deterrence against adversarial ML examples. To the best of our knowledge, we are the first to report the results of the application of the C-W attack for creating adversarial examples against various ML models for modulation classification.