Black-box Adversarial ML Attack on Modulation Classification
This work highlights a security vulnerability in communication systems using deep learning for modulation classification, but it is incremental as it applies an existing attack method to a new domain.
The authors evaluated the robustness of two deep neural network-based modulation classifiers against black-box adversarial attacks using the Carlini & Wagner method, finding that state-of-the-art classifiers are not robust, with specific attack success rates not provided.
Recently, many deep neural networks (DNN) based modulation classification schemes have been proposed in the literature. We have evaluated the robustness of two famous such modulation classifiers (based on the techniques of convolutional neural networks and long short term memory) against adversarial machine learning attacks in black-box settings. We have used Carlini \& Wagner (C-W) attack for performing the adversarial attack. To the best of our knowledge, the robustness of these modulation classifiers has not been evaluated through C-W attack before. Our results clearly indicate that state-of-art deep machine learning-based modulation classifiers are not robust against adversarial attacks.