Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159849
Title: Adversarial attacks against network intrusion detection in IoT systems
Authors: Qiu, Han
Dong, Tian
Zhang, Tianwei
Lu, Jialiang
Memmi, Gerard
Qiu, Meikang
Keywords: Engineering::Computer science and engineering
Issue Date: 2020
Source: Qiu, H., Dong, T., Zhang, T., Lu, J., Memmi, G. & Qiu, M. (2020). Adversarial attacks against network intrusion detection in IoT systems. IEEE Internet of Things Journal, 8(13), 10327-10335. https://dx.doi.org/10.1109/JIOT.2020.3048038
Journal: IEEE Internet of Things Journal
Abstract: Deep learning (DL) has gained popularity in network intrusion detection, due to its strong capability of recognizing subtle differences between normal and malicious network activities. Although a variety of methods have been designed to leverage DL models for security protection, whether these systems are vulnerable to adversarial examples (AEs) is unknown. In this article, we design a novel adversarial attack against DL-based network intrusion detection systems (NIDSs) in the Internet-of-Things environment, with only black-box accesses to the DL model in such NIDS. We introduce two techniques: 1) model extraction is adopted to replicate the black-box model with a small amount of training data and 2) a saliency map is then used to disclose the impact of each packet attribute on the detection results, and the most critical features. This enables us to efficiently generate AEs using conventional methods. With these tehniques, we successfully compromise one state-of-the-art NIDS, Kitsune: the adversary only needs to modify less than 0.005% of bytes in the malicious packets to achieve an average 94.31% attack success rate.
URI: https://hdl.handle.net/10356/159849
ISSN: 2327-4662
DOI: 10.1109/JIOT.2020.3048038
Rights: © 2020 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

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