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Modelling Enhanced Phishing detection using XGBoost

Naik, Nishant Nityanand (2021) Modelling Enhanced Phishing detection using XGBoost. Masters thesis, Dublin, National College of Ireland.

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In today's society, where everything is digitized, computers have taken over majority of human activities. Machines have a tendency to execute all of the tasks that humans were capable of, but with a greater load and in a shorter length of time. While there are advantages, the disadvantages overshadow the advantages. Phishing attacks have taken on a new face because to the internet. Phishing prevention has gone a long way as the art of phishing assaults has progressed. Adapting Machine Learning algorithms makes a significant impact in identifying and blocking phishing assaults, which would otherwise be impossible for the human brain to recognize and prevent. This paper proposes a model which is expected to give desired result. The recommended model uses complex Extreme Gradient Boost (XGBoost) algorithm to identify Phishing URLs with extreme accuracy. The result is compared with other ML algorithms like Decision Tree, Random Forest, Multilayer Perceptron, and Support Vector Machines for better analysis.

Item Type: Thesis (Masters)
Uncontrolled Keywords: Phishing; Machine Learning; XGBoost; URL
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: 03 Mar 2022 13:05
Last Modified: 03 Mar 2022 13:05

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