Enhancing Intrusion Detection Systems (IDS) using Machine Learning Techniques: A Comparative Study of Deep Learning and Classical Machine Learning Methods for Improved Detection Accuracy and Speed

Egwu, Chinedu Nelson (2024) Enhancing Intrusion Detection Systems (IDS) using Machine Learning Techniques: A Comparative Study of Deep Learning and Classical Machine Learning Methods for Improved Detection Accuracy and Speed. Masters thesis, Dublin, National College of Ireland.

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Abstract

This research proposes a comprehensive analysis on the approach of utilizing machine learning and deep learning systems to detect intrusion in a network based environment. As information technology advances, the use of malicious intent to attack networks also increases as well, therefore putting much infrastructure at the mercy of these hackers. This research examines some machine learning and deep learning algorithms which are decision trees, random forest, logistic regression, artificial neural network (ANN) and convolutional neural network (CNN), and their strengths in the detection of this intrusion in these network based systems. The key findings show that the Random Forest and the decision trees classifier, with their tree base estimator and accuracy optimization, achieved the best results with an accuracy of 98% for the both models.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Uncontrolled Keywords:	Decision trees; random forest; logistic regression; artificial neural network (ANN) and convolutional neural network (CNN); intrusion; machine learning; deep learning
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 10:58
Last Modified:	18 Jul 2025 10:58
URI:	https://norma.ncirl.ie/id/eprint/8201

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