Network Intrusion Detection using Supervised and Deep Learning Machine Learning Algorithms

Jolayemi, Ayodele Oluwagbayi (2024) Network Intrusion Detection using Supervised and Deep Learning Machine Learning Algorithms. Masters thesis, Dublin, National College of Ireland.

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Abstract

The expansion of computer networks has exacerbated worries about network security and resulted in a number of infiltration attempts. The confidentiality, integrity, and availability of data and systems are compromised by these attempts. The necessity to handle the growing risks of cyberattacks is highlighted by statistics showing an increase in the frequency of malware assaults and denial of service situations. The increase in network traffic, the complexity of Network Intrusion Detection Systems (NIDS), and the variety of protocols and data transferred via contemporary networks are the three main issues that worsen network security. The existing traditional approaches are unable to detect new types of attacks, thereby necessitating the need for more robust solutions. The goal of this research is to increase the effectiveness of machine learning and deep learning models, which include some of the most applied classification approaches, namely decision trees (DT), logistic regression (LR), naïve bayes (NB), convolutional neural networks (CNN) and recurrent neural networks (RNN). Additionally, I examined the models' performance in binary classification as well as the effects of feature significance selection and hyperparameter adjustment on the CICIDS 2017 and UNSW NB15 benchmark datasets. Based on the findings of the experiments, the optimized decision tree is the best model for a network intrusion detection system with accuracy, F1-score, and AUC score of 99.27%, 99.26%, and 99.27% respectively on the CICIDS 2017 dataset. On the UNSW NB15 dataset, the scores were 99.28% across all metrics. It outperforms other machine learning and deep learning classification techniques and underlines the superiority to traditional IDS.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Haque, Rejwanul UNSPECIFIED
Uncontrolled Keywords:	intrusion detection; machine learning; attack traffic; anomaly; network
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 B Philosophy. Psychology. Religion > BJ Ethics > Conduct of life > Reliability > Information integrity > Data integrity Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	13 Jan 2025 09:40
Last Modified:	13 Jan 2025 09:40
URI:	https://norma.ncirl.ie/id/eprint/7309

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