COMPARISON OF CLASSIFICATION ALGORITHMS TO DETECT PHISHING WEB PAGES USING FEATURE SELECTION AND EXTRACTION

Abstract

The phishing is a kind of e-commerce lure which try to steal the confidential information of the web user by making identical website of legitimate one in which the contents and images almost remains similar to the legitimate website with small changes. Another way of phishing is to make minor changes in the URL or in the domain of the legitimate website. In this paper, a number of anti-phishing toolbars have been discussed and proposed a system model to tackle the phishing attack. The proposed anti-phishing system is based on the development of the Plug-in tool for the web browser. The performance of the proposed system is studied with three different data mining classification algorithms which are Random Forest, Nearest Neighbour Classification (NNC), Bayesian Classifier (BC). To evaluate the proposed anti-phishing system for the detection of phishing websites, 7690 legitimate websites and 2280 phishing websites have been collected from authorised sources like APWG database and PhishTank. After analyzing the data mining algorithms over phishing web pages, it is found that the Bayesian algorithm gives fast response and gives more accurate results than other algorithms.