Enhancing Real-Time Threat Detection in Data Centre Firewalls Through Deep Learning & Machine Learning Techniques

Sarfaraz, Muhammad Usman (2024) Enhancing Real-Time Threat Detection in Data Centre Firewalls Through Deep Learning & Machine Learning Techniques. Masters thesis, Dublin, National College of Ireland.

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Abstract

Cyber-crime has become one of the most significant risks known in the present world where the issue of connectivity and technological advancement is paramount coupled with the concern of data flow across the globe, which is why the protection of computer networks is of great significance. Traditional firewalls, like security guards of a network operated on a set of simple rules that controlled the network traffic, were effective when designed in the late 1980s. Yet, for some reason, the modern-day methods employed by cybercriminals have changed concurrently with the advancement of modern technology. This thesis aims to analyse the possibility of using machine learning, a subfield of artificial intelligence, in security system systems as a new perspective on network protection. The objective was to design a better intelligent security system capable of distinguishing between malicious actions and blocking them. Using real-time data analysis, the new security system can quickly change its security rules to stop an attack. To this end, the actual statistical information about the traffic in the network was gathered and analysed in real time. Machine learning algorithms were then used to learn features corresponding to cyber threats. These models were incorporated within the security system in a way that it could automatically escalate its security system. As these findings indicated, the newly applied approach enhanced the firewall efficiency in identifying and blocking cyber threats. From this research, it is evident that the profession can apply state-of-the-art technology to improve the protection of the networks to make the world behind computers safer from evolving dangers.

Item Type:	Thesis (Masters)
Supervisors:	Name Email McCabe, Liam UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence 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:	31 Jul 2025 08:47
Last Modified:	31 Jul 2025 08:47
URI:	https://norma.ncirl.ie/id/eprint/8366

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