Feature selection for intrusion detection systems

dc.contributor.author Kamalov, Firuz
dc.contributor.author Moussa, Sherif
dc.contributor.author Zgheib, Rita
dc.contributor.author Mashaal, Omar
dc.date.accessioned 2021-02-28T13:04:12Z
dc.date.available 2021-02-28T13:04:12Z
dc.date.copyright © 2020
dc.date.issued 2020-12
dc.description This 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/ISCID51228.2020.00065 en_US
dc.description.abstract In 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.citation Kamalov, 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/ISCID51228.2020.00065 en_US
dc.identifier.isbn 978-172818446-3
dc.identifier.uri https://doi.org/10.1109/ISCID51228.2020.00065
dc.identifier.uri http://hdl.handle.net/20.500.12519/345
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation Authors 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.ispartofseries 2020 13th International Symposium on Computational Intelligence and Design (ISCID);
dc.rights Permission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.
dc.rights.holder Copyright : 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.uri https://www.ieee.org/publications/rights/rights-policies.html
dc.subject Feature selection en_US
dc.subject intrusion detection system en_US
dc.subject Network security en_US
dc.subject Random forest en_US
dc.subject Computer crime en_US
dc.subject Intelligent computing en_US
dc.subject Intrusion detection en_US
dc.subject Benchmark selection en_US
dc.subject Continuous input en_US
dc.subject Detection system en_US
dc.subject Feature selection methods en_US
dc.subject Intrusion Detection Systems en_US
dc.subject Key elements en_US
dc.subject Network traffic en_US
dc.subject Target values en_US
dc.subject Feature extraction en_US
dc.title Feature selection for intrusion detection systems en_US
dc.type Conference Paper en_US
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