Orthogonal variance-based feature selection for intrusion detection systems

Files

Access Instruction 640.pdf (56.37 KB)

Date

2021

Authors

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

URI

https://doi.org/10.1109/ISNCC52172.2021.9615656
 http://hdl.handle.net/20.500.12519/640

DOI

Collections

Department of Electrical Engineering