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Phishing detection using machine learning

Wandhare, Suraj (2020) Phishing detection using machine learning. Masters thesis, Dublin, National College of Ireland.

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Phishing attacks cause a loss of millions of dollars every year. It involves social engineering which makes it much more effective. There are many proposed solutions to solve the problem of phishing using machine learning. This research is partly a study to compare different supervised machine learning algorithms to find the optimal algorithm for phishing detection using machine learning and partly to address the issue of URL shortening service exploitation. URL shortening is used on a daily basis to help reduce the size of the URL, but attackers use these services to hide the original URL of the phishing website. In this research we propose an application that takes in a URL, uses multiple feature extraction techniques to determine whether the URL is phishing or not. We propose a URL unshortener which will return the original URL, compliments the machine learning algorithm increasing its accuracy. The initial accuracy is 92.6% and the False Positive rate is 0.4% but when the URL unshortener is added the accuracy is increased to 96.6%.

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
T Technology > T Technology (General) > Information Technology > Computer software
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: Caoimhe Ní Mhaicín
Date Deposited: 02 Apr 2020 11:15
Last Modified: 02 Apr 2020 11:15

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