@inproceedings{d3aa3e2ab5b6400bbfdfaef855226cd4,
title = "Adversarial Machine Learning Attack on Modulation Classification",
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.",
keywords = "Adversarial machine learning, Modulation classification",
author = "Muhammad Usama and Muhammad Asim and Junaid Qadir and Ala Al-Fuqaha and Imran, \{Muhammad Ali\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 UK/China Emerging Technologies, UCET 2019 ; Conference date: 21-08-2019 Through 22-08-2019",
year = "2019",
month = aug,
doi = "10.1109/UCET.2019.8881843",
language = "English",
series = "2019 UK/China Emerging Technologies, UCET 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2019 UK/China Emerging Technologies, UCET 2019",
address = "United States",
}