Improving the classification rate for detecting Malicious URL using Ensemble Learning Methods

Bingi, Santosh Raj (2021) Improving the classification rate for detecting Malicious URL using Ensemble Learning Methods. Masters thesis, Dublin, National College of Ireland.

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Abstract

The surge in the use of the internet has created the biggest hurdle for the security of the digital world. Malicious URLs are the main source of performing phishing activities, transmission of viruses such as trojans, worms etc. Various malicious URLs try to retrieve user information by releasing distinct malicious software. A legitimate user who cannot detect and remove malicious URLs by end-users can leave them vulnerable. Malicious URLs also allow attackers to gain unauthorized access to user data. Therefore, it is essential step to identify the countermeasures for stopping such activities with the help of new and advance technologies. In order to correctly identify the URL as Malicious or benign, machine learning based methods has been considered as one of the efficient approach. However, using the machine learning approach the number of false positive and false negative outcomes are found to be more. Hence, there is still a scope of improvement for identifying correctly the URL as Malicious or Benign. In this research, a extended version of machine learning methods has been proposed where the properties of two or more models are combined, can be referred as ensemble learning methods. Using ensemble learning methods, we were able to achieve more accurate and better results.

Item Type:	Thesis (Masters)
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 H Social Sciences > HV Social pathology. Social and public welfare > Criminology > Crimes and Offences > Cyber Crime
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Clara Chan
Date Deposited:	18 Oct 2021 14:04
Last Modified:	18 Oct 2021 14:04
URI:	https://norma.ncirl.ie/id/eprint/5102

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