THE DESIGN OF PHISHING EMAIL DETECTION BY USING MACHINE LEARNING TECHNIQUES

THE DESIGN OF PHISHING EMAIL DETECTION BY USING MACHINE LEARNING TECHNIQUES

Electronic mail (e-mail) is one of the most popular methods for online communication and data transmission over the web because of its rapid and simple dissemination of data, cheap distribution cost, and permanence. Despite its advantages, e-mail has several drawbacks. The most common of these are phishing and spam emails. While both phishing emails and spam can jam your inbox, only phishing is specifically designed to steal login passwords and other important information. Spam is a marketing strategy that involves sending unsolicited emails to large groups of people in order to promote products and services. A phishing email is a genuine-looking email that is intended to fool users into thinking it is a legitimate email and then either expose sensitive information or download malicious software by clicking on malicious links contained in the email’s body. Phishing is more harmful in this aspect because it has caused tremendous financial loss to domain users. Therefore, there is an urgent need for phishing email detection with high accuracy. Banking information, credit reports, login data, and other sensitive and personal information are frequently transmitted over email. This makes them valuable to cyber criminals, who can exploit the knowledge for their own gain. In this paper, we proposed a phishing email detection algorithm based on Naïve Base algorithms and a Support Vector Machine classifier. We extracted email features by analyzing the email header structure, email body, email Uniform Resource Locator information and email script function features. The aim of this paper: (i) Investigate the challenge of the existing email filtration method for the purpose of minimizing the gap caused by junk mail filtration; (ii) Provide an effective and improved way of phishing email classification method by using machine learning approaches; (iii) Prevent users from opening the malicious link and responding to the attacker; and (iv) Prevent phishing emails from being sent to the intended recipient. Experiments are performed on a dataset consisting of a total of 5229, which includes 4115 legitimate emails and 1114 phishing emails. The proposed technique performed well in detecting phishing emails. According to our findings, Support Vector Machines outperformed the Naive Base in detecting phishing emails, with accuracy rates of 98.76% and 97.51%, respectively.

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