@inproceedings{e86bc11ea03d44b3b770abeee443d523,
title = "Generative adversarial networks for launching and thwarting adversarial attacks on network intrusion detection systems",
abstract = "Intrusion detection systems (IDSs) are an essential cog of the network security suite that can defend the network from malicious intrusions and anomalous traffic. Many machine learning (ML)-based IDSs have been proposed in the literature for the detection of malicious network traffic. However, recent works have shown that ML models are vulnerable to adversarial perturbations through which an adversary can cause IDSs to malfunction by introducing a small impracticable perturbation in the network traffic. In this paper, we propose an adversarial ML attack using generative adversarial networks (GANs) that can successfully evade an ML-based IDS. We also show that GANs can be used to inoculate the IDS and make it more robust to adversarial perturbations.",
keywords = "Adversarial machine learning, GAN, IDS",
author = "Muhammad Usama and Muhammad Asim and Siddique Latif and Junaid Qadir and Ala-Al-Fuqaha",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 15th IEEE International Wireless Communications and Mobile Computing Conference, IWCMC 2019 ; Conference date: 24-06-2019 Through 28-06-2019",
year = "2019",
month = jun,
doi = "10.1109/IWCMC.2019.8766353",
language = "English",
series = "2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "78--83",
booktitle = "2019 15th International Wireless Communications and Mobile Computing Conference, IWCMC 2019",
address = "United States",
}