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Intrusion Detection Systems Using Machine Learning

Taylor, William; Hussain, Amir; Gogate, Mandar; Dashtipour, Kia; Ahmad, Jawad

Authors

William Taylor



Contributors

Wadii Boulila
Editor

Anis Koubaa
Editor

Maha Driss
Editor

Imed Riadh Farah
Editor

Abstract

Intrusion detection systems (IDS) have developed and evolved over time to form an important component in network security. The aim of an intrusion detection system is to successfully detect intrusions within a network and to trigger alerts to system administrators. Machine learning is a method of detecting patterns in sets of data in order that such patterns can be recognised in new unseen data. This method can be employed by intrusion detection systems whereby datasets that contain attacks can be used to train machine learning models, which in turn facilitates the implementation of such models to detect identical attacks in previously unseen data. This paper compares various machine learning algorithms using binary, multiclass and ensemble-based classification on the KDD CUP 99 and CICIDS 2017 datasets. This paper also makes comparisons between full and reduced features. Findings conclude that the Random Forest machine learning algorithm produces high accuracy in all experiments. Random Forest was able to provide efficient execution times which benefits from the reduced features.

Citation

Taylor, W., Hussain, A., Gogate, M., Dashtipour, K., & Ahmad, J. (2024). Intrusion Detection Systems Using Machine Learning. In W. Boulila, J. Ahmad, A. Koubaa, M. Driss, & I. Riadh Farah (Eds.), Decision Making and Security Risk Management for IoT Environments (75-98). Springer. https://doi.org/10.1007/978-3-031-47590-0_5

Online Publication Date Oct 10, 2023
Publication Date Feb 22, 2024
Deposit Date May 2, 2024
Publisher Springer
Peer Reviewed Peer Reviewed
Pages 75-98
Series Title Advances in Information Security
Series Number 106
Series ISSN 1568-2633
Book Title Decision Making and Security Risk Management for IoT Environments
ISBN 978-3-031-47589-4
DOI https://doi.org/10.1007/978-3-031-47590-0_5
Public URL http://researchrepository.napier.ac.uk/Output/3632664