Kamalov, FiruzMoussa, SherifKhatib, Ziad ElMnaouer, Adel Ben2022-05-182022-05-18© 20212021Kamalov, 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/ISNCC52172.2021.9615656978-073811316-6https://doi.org/10.1109/ISNCC52172.2021.9615656http://hdl.handle.net/20.500.12519/640This 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/ISNCC52172.2021.9615656In 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.en-USPermission to reuse abstract has been secured from Institute of Electrical and Electronics Engineers Inc.Intrusion detection systemNetwork securityNeural networkVariance decompositionConference PaperCopyright : © 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.