Title: A new wrapper feature selection model for anomaly-based intrusion detection systems

Authors: Meriem Kherbache; Kamal Amroun; David Espes

Addresses: LIMED Laboratory, Faculty of Exact Sciences, University Abderrahmane Mira, Bejaia, Algeria ' LIMED Laboratory, Faculty of Exact Sciences, University Abderrahmane Mira, Bejaia, Algeria ' University of Western Brittany, 29238 Brest, France

Abstract: Feature selection is a fundamental phase of anomaly-based intrusion detection. It is a method that selects the near-optimal subset of features to improve efficiency and reduce the number of false positives. This paper presents a new method that combines agglomerative hierarchical clustering (AHC) with a support vector machine (SVM) classifier. An intelligent process classifies features based on their variances for each attack category. Features are selected based on their variance and grouped according to their similarities. An iterative algorithm forms subsets of candidate combinations by combining the obtained attack clusters with the normal ones. The SVM classifier is applied to find the best candidate. NSL-KDD and CICIDS2017 datasets are used. The results show a significant reduction in the number of features. Moreover, it performs very well on all attacks and outperforms other existing approaches. Perfect accuracy of 100% is achieved on Heartbleed, SQL injection, and botnet attacks.

Keywords: intrusion detection system; IDS; feature selection; agglomerative hierarchical clustering; AHC; support vector machine; SVM.

DOI: 10.1504/IJSN.2022.123298

International Journal of Security and Networks, 2022 Vol.17 No.2, pp.107 - 123

Received: 05 Mar 2020
Accepted: 17 May 2021
Published online: 08 Jun 2022 *

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