Feature selection for intrusion detection systems

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dc.contributor.authorKamalov, Firuz
dc.contributor.authorMoussa, Sherif
dc.contributor.authorZgheib, Rita
dc.contributor.authorMashaal, Omar
dc.date.accessioned2021-02-28T13:04:12Z
dc.date.available2021-02-28T13:04:12Z
dc.date.copyright© 2020
dc.date.issued2020-12
dc.descriptionThis conference paper is not available at CUD collection. The version of scholarly record of this conference paper is published in 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (2020), available online at: https://doi.org/10.1109/ISCID51228.2020.00065en_US
dc.description.abstractIn this paper, we analyze existing feature selection methods to identify the key elements of network traffic data that allow intrusion detection. In addition, we propose a new feature selection method that addresses the challenge of considering continuous input features and discrete target values. We show that the proposed method performs well against the benchmark selection methods. We use our findings to develop a highly effective machine learning-based detection systems that achieves 99.9% accuracy in distinguishing between DDoS and benign signals. We believe that our results can be useful to experts who are interested in designing and building automated intrusion detection systems. ©2020 IEEE.en_US
dc.identifier.citationKamalov, F., Moussa, S., Zgheib, R., & Mashaal, O. (2020, December). Feature selection for intrusion detection systems. In 2020 13th International Symposium on Computational Intelligence and Design (ISCID) (pp. 265-269). IEEE. https://doi.org/10.1109/ISCID51228.2020.00065en_US
dc.identifier.isbn978-172818446-3
dc.identifier.urihttps://doi.org/10.1109/ISCID51228.2020.00065
dc.identifier.urihttp://hdl.handle.net/20.500.12519/345
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.relationAuthors Affiliations : Kamalov, F., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Moussa, S., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Zgheib, R., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates; Mashaal, O., Department of Electrical Engineering, Canadian University Dubai, Dubai, United Arab Emirates
dc.relation.ispartofseries2020 13th International Symposium on Computational Intelligence and Design (ISCID);
dc.rightsPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holderCopyright : 2020 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.subjectFeature selectionen_US
dc.subjectintrusion detection systemen_US
dc.subjectNetwork securityen_US
dc.subjectRandom foresten_US
dc.subjectComputer crimeen_US
dc.subjectIntelligent computingen_US
dc.subjectIntrusion detectionen_US
dc.subjectBenchmark selectionen_US
dc.subjectContinuous inputen_US
dc.subjectDetection systemen_US
dc.subjectFeature selection methodsen_US
dc.subjectIntrusion Detection Systemsen_US
dc.subjectKey elementsen_US
dc.subjectNetwork trafficen_US
dc.subjectTarget valuesen_US
dc.subjectFeature extractionen_US
dc.titleFeature selection for intrusion detection systemsen_US
dc.typeConference Paperen_US

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