Orthogonal variance-based feature selection for intrusion detection systems

Adel Ben Mnaouer; Firuz Kamalov; Sherif Moussa; Ziad El Khatib

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Orthogonal variance-based feature selection for intrusion detection systems

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arxiv 2110.12627 v1 pith:Q7Z3F3BJ submitted 2021-10-25 cs.CR cs.LG

Firuz Kamalov , Sherif Moussa , Ziad El Khatib , Adel Ben Mnaouer This is my paper · ORCID

classification cs.CRcs.LG

keywords detectionintrusionfeaturesmethodnetworkorthogonalproposedaccuracy

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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.

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