Orthogonal variance-based feature selection for intrusion detection systems

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorMoussa, Sherif
dc.contributor.authorKhatib, Ziad El
dc.contributor.authorMnaouer, Adel Ben
dc.date.accessioned2022-05-18T13:07:03Z
dc.date.available2022-05-18T13:07:03Z
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 Symposium on Networks, Computers and Communications, ISNCC (2021), available online at: https://doi.org/10.1109/ISNCC52172.2021.9615656
dc.description.abstractIn this paper, we apply a fusion machine learning method to construct an automatic intrusion detection system. Concretely, we employ the orthogonal variance decomposition technique to identify the relevant features in network traffic data. The selected features are used to build a deep neural network for intrusion detection. The proposed algorithm achieves 100% detection accuracy in identifying DDoS attacks. The test results indicate a great potential of the proposed method. © 2021 IEEE.
dc.description.sponsorshipNational Natural Science Foundation of China National Key Research and Development Program of China
dc.identifier.citationKamalov, F., Moussa, S., Khatib, Z. E., & Mnaouer, A. B. (2021). Orthogonal variance-based feature selection for intrusion detection systems. 2021 International Symposium on Networks, Computers and Communications, ISNCC. https://doi.org/10.1109/ISNCC52172.2021.9615656
dc.identifier.isbn978-073811316-6
dc.identifier.urihttps://doi.org/10.1109/ISNCC52172.2021.9615656
dc.identifier.urihttp://hdl.handle.net/20.500.12519/640
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; Moussa, S., Canadian University Dubai, Department Of Electrical Engineering, Dubai, United Arab Emirates; Khatib, Z.E., Canadian University Dubai, Department Of Electrical Engineering, Dubai, United Arab Emirates; Mnaouer, A.B., Canadian University Dubai, Department Of Computer Engineering, Dubai, United Arab Emirates
dc.relation.ispartofseries2021 International Symposium on Networks, Computers and Communications, ISNCC
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.subjectIntrusion detection system
dc.subjectNetwork security
dc.subjectNeural network
dc.subjectVariance decomposition
dc.titleOrthogonal variance-based feature selection for intrusion detection systems
dc.typeConference Paper
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