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dc.contributor.authorQiu, Hanen_US
dc.contributor.authorDong, Tianen_US
dc.contributor.authorZhang, Tianweien_US
dc.contributor.authorLu, Jialiangen_US
dc.contributor.authorMemmi, Gerarden_US
dc.contributor.authorQiu, Meikangen_US
dc.identifier.citationQiu, 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.
dc.description.abstractDeep 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.en_US
dc.relation.ispartofIEEE Internet of Things Journalen_US
dc.rights© 2020 IEEE. All rights reserved.en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleAdversarial attacks against network intrusion detection in IoT systemsen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.subject.keywordsFeature Extractionen_US
dc.subject.keywordsComputational Modelingen_US
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