Adversarial Machine Learning Attack on Modulation Classification
This highlights a critical vulnerability in cognitive self-driving networks, though it is incremental as it applies an existing attack to a new domain.
The paper evaluated the robustness of 9 ML-based modulation classifiers against the Carlini & Wagner attack, showing they provide no deterrence against adversarial examples.
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.