Title: Network intrusion detection using meta-heuristic feature selection and cost-sensitive learning

Authors: Ritinder Kaur; Neha Gupta

Addresses: Manav Rachna International Institute of Research and Studies, Faridabad, India ' Faculty of Computer Applications, Manav Rachna International Institute of Research and Studies, Faridabad, India

Abstract: In contemporary society, networked computers are playing a pivotal role in dissemination of knowledge and critical data in information systems; ensuring its security has become a challenging task for the network administrators and researchers. Machine learning techniques are extensively used in intrusion detection systems to mine out the extensive network data and extrapolate attack patterns. This paper proposes an intrusion detection framework with a combination of diverse attribute selection algorithms and machine learning algorithms to provide effective intrusion detection. Firstly, the model extracts the most relevant attributes using a hybrid meta-heuristic feature selection algorithm and then applies supervised machine learning algorithms to detect the several attack classes with improved detection accuracy, execution time and error rate. This study used NSL-KDD dataset on the proposed CS-CFSMHA framework with AdaBoost ensemble technique. Cost-sensitive classification was applied which improved the minority class accuracy and the overall accuracy to 81.1%.

Keywords: machine learning; intrusion detection; feature selection; NSL-KDD; swarm techniques; meta-heuristic search: cost-sensitive learning.

DOI: 10.1504/IJITST.2023.129572

International Journal of Internet Technology and Secured Transactions, 2023 Vol.13 No.2, pp.105 - 138

Received: 15 Sep 2021
Accepted: 26 Jun 2022
Published online: 14 Mar 2023 *

Full-text access for editors Full-text access for subscribers Purchase this article Comment on this article