Comparative Analysis of Supervised Machine Learning Models for Phishing Detection

Sawant, Mamta Sanjay (2021) Comparative Analysis of Supervised Machine Learning Models for Phishing Detection. Masters thesis, Dublin, National College of Ireland.

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Abstract

Phishing is an illegitimate way of extracting information from the target computer system. The massive use of online services motivated the higher number of online fraudulent activities. Phishing is a technique that is used by individuals to do fraudulent activities, and this is considered one of the most dangerous cyber attacking techniques. Machine learning (ML) techniques are frequently used to solve real-world problems related to classification, detection, and regression. This report focused on a comparative analysis of the machine learning models to detect Phishing websites. Three major supervised machine learning algorithms such as KNN, Decision Tree, and Logistic Regression are used to build an ML model for detection of the Phishing website and their results are compared. The dataset is collected from the Mendeley Data site, and it comprises a total of 88647 observations. The result of this study shows that the highest accuracy is recorded for the Logistic Regression and the lowest accuracy is obtained for the KNN model with respect to parameter metrics such as accuracy and time taken to train the model.

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 Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions:	School of Computing > Master of Science in Cyber Security
Depositing User:	Tamara Malone
Date Deposited:	22 Dec 2022 11:19
Last Modified:	07 Mar 2023 16:16
URI:	https://norma.ncirl.ie/id/eprint/6020

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