Orthogonal variance-based feature selection for intrusion detection systems

Authors: Firuz Kamalov, Sherif Moussa, Ziad El Khatib, Adel Ben Mnaouer
Published: 2021-10-25

Source: https://arxiv.org/abs/2110.12627

PDF: https://arxiv.org/pdf/2110.12627

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機械学習アルゴリズム 特徴抽出手法 MLベースのIDS

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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 identifying DDoS attacks. The test results indicate a great potential of the proposed method.