Feature selection and deep learning approach for anomaly network intrusion detection
by Khadidja Bennaceur; Zakaria Sahraoui; Mohamed Ahmad Nacer
International Journal of Information and Computer Security (IJICS), Vol. 23, No. 4, 2024

Abstract: The growth of internet-scale systems and data as well as increased cyber attacks call for more robust security solution. A crucial element in this regard is network intrusion detection system (NIDS), which analyses network traffic to detect attacks. However, a key challenge for NIDS is real-time detection of all possible attacks. The present work proposes a new approach, Chi2-GRU-MLP, to build an efficient anomaly network intrusion detection. With this innovative approach, we harness the capabilities of two deep learning models, namely gated recurrent unit and multi-layer perceptron, to make a suitable supervised classifier. Additionally, the uniqueness of this proposal lies in its utilisation of dependency feature selection that is used together with a proposed selection method for pre-processing the network traffic. This combination is designed to boost the efficiency and the acceleration of the proposed approach. The proposed approach is compared with other recent anomaly network intrusion detection works in terms of several metrics and using three different well-known datasets such as NSL-KDD, CICIDS-2017 and UNSW-NB15. Comparison shows that our proposed approach significantly outperforms earlier ANIDS.

Online publication date: Mon, 10-Jun-2024

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