Detecting Malicious URL using Extreme Learning Machine Algorithm

Patel, Ankit (2022) Detecting Malicious URL using Extreme Learning Machine Algorithm. Masters thesis, Dublin, National College of Ireland.

Preview	PDF (Master of Science) Download (821kB) | Preview
Preview	PDF (Configuration manual) Download (906kB) | Preview

Abstract

One of the major threats for various companies is phishing attacks which utilize malicious URLs as their payloads for a successful attack. Traditional malicious URL detection systems typically used blacklisting-based approaches or signature-based approaches. Such approaches can be bypassed easily by changing the signature pattern in the URL, hence insufficient for detecting newly generated phishing or malicious URLs. Hence many systems are implementing machine learning, deep learning, neural networks, and AI-based approaches in their detection systems to make the system more accurate and faster as compared to the traditional approach. Many machine learning and deep learning algorithms are used for such research but for this proposed model, the Extreme Learning Machine algorithm is used with the sigmoid function being used in the activation function instead of the reLu function. For training the model twenty characteristics of the URL are used. The model provided 84% accuracy with 1.48 minutes of time taken to train the model which is much faster as compared to using reLu function.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Salahuddin, Jawad UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Algebra > Algorithms > Computer algorithms Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Z Bibliography. Library Science. Information Resources > ZA Information resources > ZA4150 Computer Network Resources > The Internet T Technology > TK Electrical engineering. Electronics. Nuclear engineering > Telecommunications > The Internet
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	04 May 2023 15:41
Last Modified:	04 May 2023 15:41
URI:	https://norma.ncirl.ie/id/eprint/6537

Actions (login required)

View Item