A Phishing e-mail detection approach using machine learning techniques

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University of New Brunswick


According to APWG reports of 2014 and 2015, the number of unique Phishing e-mail reports received from consumers has increased tremendously from 68270 e-mails in October 2014 to 106421 e-mails in September 2015. This significant increase is a proof of the existence of Phishing attacks and the high rate of damages they have caused to Internet users in the past. Because no attention is made in the literature to specifically detect Phishing e-mails related to advertisement and pornographic, attackers are becoming extremely intelligent to use these means of attraction to track users and adjusting their attacks base on users behaviours and hot topics extracted from community news and journals. We focus on detecting deceptive e-mail which is a form of Phishing attacks by proposing a novel framework to accurately identify not only e-mail Phishing attacks but also advertisements or pornographic e-mails consider as attracting ways to launch Phishing. Our approach known as Phishing Alerting System (PHAS) has the ability to detect and alert all type of deceptive e-mails so as to help users in decision making. We are using a well known e-mail dataset and base on our extracted features we are able to obtain about 93.11% accuracy while using machine learning techniques such as J48 Decision Tree and KNN. Furthermore, we equally evaluate our system built based on these above features and obtained approximately the same accuracy while using the same dataset as input to our system.