Orthogonal variance-based feature selection for intrusion detection systems

• URL: http://arxiv.org/abs/2110.12627v1
• Date: Mon, 25 Oct 2021 04:07:53 GMT
• Title: Orthogonal variance-based feature selection for intrusion detection systems
• Authors: Firuz Kamalov, Sherif Moussa, Ziad El Khatib, Adel Ben Mnaouer
• Abstract summary: We apply a fusion machine learning method to construct an automatic intrusion detection system. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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