A Comparative Study on Email Phishing Detection Using Machine Learning Techniques

Abstract

Phishing Email can be described as an email that looks exactly like a legitimate email, but it is designed by phisher with an intention to deceive the email’s user. The purpose of phishing email is to trick email user to visit fake website that looks exactly like a real one or to trick user to download the available attachment in the email without knowing that they are downloading virus into their machine. As the number of phishing emails are increasing from day to day and due to the complexity in detecting phishing email, there are numbers of continuous researches that have been done to improve existing detection tools or to develop a new one. To provide a thorough understanding of phishing attacks, this paper provides a brief explanation on phishing email and phishing attack. This paper presents the comparison of previous studies in commonly used Supervised Machine Learning techniques on detecting the phishing email attack such as Decision Tree (DT), Naive Bayes (NB), Random Forest (RF), and Support Vector machine(SVM). The findings of this study concluded that SVM and RF are the best techniques that can be used to detect phishing email.