Orthogonal variance-based feature selection for intrusion detection systems

Date
2021
Authors
Kamalov, Firuz
Moussa, Sherif
Khatib, Ziad El
Mnaouer, Adel Ben
Journal Title
Journal ISSN
Volume Title
Publisher
Institute of Electrical and Electronics Engineers Inc.
Abstract
In 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.
Description
This 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/ISNCC52172.2021.9615656
Keywords
Intrusion detection system, Network security, Neural network, Variance decomposition
Citation
Kamalov, 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/ISNCC52172.2021.9615656