An evaluation of Network Intrusion Detection Systems

Kaweesa, James (2023) An evaluation of Network Intrusion Detection Systems. Masters thesis, Dublin, National College of Ireland.

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Abstract

In information systems and networks, detecting intrusions is a crucial yet challenging task. This increased the need for effective ways to identify intrusions to safeguard these systems. Existing intrusion detection system (IDS) models have achieved notable performance, but frequently struggle to identify multiple types of attacks due to lengthy classifier training. Comprehensive analysis and evaluation of anomaly-based and signature-based intrusion detection system was conducted. This study investigates two Intrusion Detection System (IDS) techniques, signature-based IDS for known attacks and anomaly-based IDS using unsupervised machine learning algorithms, specifically the Isolation Forest. The goal is to develop and implement an efficient IDS, considering performance metrics like precision, recall, and F1-score. The focus is on effectively detecting and responding to both known and unknown attacks to enhance network security and cyber threat prevention.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Pantridge, Michael UNSPECIFIED
Uncontrolled Keywords:	IDS; Anomaly-based; Signature-based; Machine learning algorithms; Cyber-attacks
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > Computer networks Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	22 Oct 2024 14:47
Last Modified:	22 Oct 2024 14:47
URI:	https://norma.ncirl.ie/id/eprint/7126

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