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 |