Phishing Web Pages Detection Using Feature Selection and Extraction Method

Phishing is a security attack that involves obtaining sensitive information as a trustworthy entity. The user tries to steal the confidential information of the web user such as online banking passwords, credit card number and other financial data by making identical website of legitimate one in which the contents and images almost remains similar to the legitimate website with small changes. In this paper, a number of anti-phishing toolbars have been discussed and proposed a system model to tackle the phishing attack. 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. The motivation of our study is to propose a safer framework for detecting phishing websites with high accuracy in less time.