Advanced ML Approaches for Intrusion Detection: A Comprehensive Analysis Using UNSW-NB15 and NSL-KDD Datasets

Gade, Lourdu Mary (2024) Advanced ML Approaches for Intrusion Detection: A Comprehensive Analysis Using UNSW-NB15 and NSL-KDD Datasets. Masters thesis, Dublin, National College of Ireland.

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Abstract

The exponentially of complex connecting systems in the information age has brought new and more difficult challenges in the cyber defence, as current Intrusion Detection Systems that rely solely on static attack signatures fail to protect against zero day attacks or advanced persistent threats (APTs). This thesis proposes a method of anomaly detection in networks that are part of large Systems of Systems SoS without prior definition of specific signatures of attacks, using the ML ML techniques. This study aims at improving the performance and precision of intrusion detection using benchmark classifiers including Random Forest, XGBoost, and Support Vector Machines (SVM), as well as benchmark datasets including NSL-KDD and UNSW-NB15. Pearson correlation factor is used in feature selection together with methods such as recursive feature elimination in order to refine inputs for enhancing the general model. The models are testing thoroughly for the accuracy, precision, recall, and F1-score that gives helpful information about discovering both known and new threats in cyber. Regarding critical issues, for example, high false-positive rates, or the need for further development of non-specific IDS models that would be able to address new threats so prevalent every time more sophisticated network structures are used, this research proposes the solutions for further development of highly effective, virtually non-resource-consuming IDS systems. The results have demonstrated that ML can be implemented as a strategic innovation in cybersecurity research and highlights a model for developing intelligent systems to counter contemporary threats.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Hafeez, Khadija UNSPECIFIED
Uncontrolled Keywords:	Network Security; Intrusion Detection; NSL-KDD; NSL-KDD; UNSW-NB15; ML; SVM; Random Forest; XGBoost
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Ciara O'Brien
Date Deposited:	18 Jul 2025 11:25
Last Modified:	18 Jul 2025 11:25
URI:	https://norma.ncirl.ie/id/eprint/8204

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