Kamalov, FiruzMoussa, SherifZgheib, RitaMashaal, Omar2021-02-282021-02-28© 20202020-12Kamalov, 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/ISCID51228.2020.00065978-172818446-3https://doi.org/10.1109/ISCID51228.2020.00065http://hdl.handle.net/20.500.12519/345This 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/ISCID51228.2020.00065In 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.enPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Feature selectionintrusion detection systemNetwork securityRandom forestComputer crimeIntelligent computingIntrusion detectionBenchmark selectionContinuous inputDetection systemFeature selection methodsIntrusion Detection SystemsKey elementsNetwork trafficTarget valuesFeature extractionConference PaperCopyright : 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.