Autoencoder-based Intrusion Detection System

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorZgheib, Rita
dc.contributor.authorLeung, Ho Hon
dc.contributor.authorAl-Gindy, Ahmed
dc.contributor.authorMoussa, Sherif
dc.date.accessioned2022-05-18T14:12:38Z
dc.date.available2022-05-18T14:12:38Z
dc.date.copyright© 2021
dc.date.issued2021
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this paper is published in 2021 International Conference on Engineering and Emerging Technologies (ICEET) (2021), available online at: https://doi.org/10.1109/ICEET53442.2021.9659562
dc.description.abstractGiven the dependence of the modern society on networks, the importance of effective intrusion detection systems (IDS) cannot be underestimated. In this paper, we consider an autoencoder-based IDS for detecting distributed denial of service attacks (DDoS). The advantage of autoencoders over traditional machine learning methods is the ability to train on unlabeled data. As a result, autoencoders are well-suited for detecting unknown attacks. The key idea of the proposed approach is that anomalous traffic flows will have higher reconstruction loss which can be used to flag the intrusions. The results of numerical experiments show that the proposed method outperforms benchmark unsupervised algorithms in detecting DDoS attacks. © 2021 IEEE.
dc.identifier.citationKamalov, F., Zgheib, R., Leung, H. H., Al-Gindy, A., & Moussa, S. (2021). Autoencoder-based intrusion detection system. 2021 International Conference on Engineering and Emerging Technologies (ICEET). https://doi.org/10.1109/ICEET53442.2021.9659562
dc.identifier.isbn978-166542714-2
dc.identifier.urihttps://doi.org/10.1109/ICEET53442.2021.9659562
dc.identifier.urihttp://hdl.handle.net/20.500.12519/643
dc.language.isoen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.relationAuthors Affiliations : Kamalov, F., Canadian University Dubai, Department of Electrical Engineering, Dubai, United Arab Emirates; Zgheib, R., Science Canadian University Dubai, Department of Computer, Dubai, United Arab Emirates; Leung, H.H., UAE University, Department of Mathematics, Al Ain, United Arab Emirates; Al-Gindy, A., Canadian University Dubai, Department of Electrical Engineering, Dubai, United Arab Emirates; Moussa, S., Canadian University Dubai, Department of Electrical Engineering, Dubai, United Arab Emirates
dc.relation.ispartofseries2021 International Conference on Engineering and Emerging Technologies (ICEET)
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.rights.urihttps://www.ieee.org/publications/rights/rights-policies.html
dc.subjectAnomaly detection
dc.subjectAutoencoders
dc.subjectCybersecurity
dc.subjectIntrusion detection systems
dc.subjectUnsupervised learning
dc.titleAutoencoder-based Intrusion Detection System
dc.typeConference Paper
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