Manuscript Number : IJSRCE182313
Phishing Web Pages Detection Using Feature Selection and Extraction Method
Authors(2) :-Ritika Arora, Ashok Kumar Arora
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.
Ritika Arora
Assistant Professor Panjab University SSG Regional Centre Hoshiarpur, Punjab, India
Ashok Kumar Arora
Superintending Engineer, Water Resources Department (Pb. Irrigation ),Govt of Punjab, Mohali, Punjab, India
Phishing, Anti-Phishing , Add-on For Web Browser, Data Mining Classification Algorithms.
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Publication Details
Published in : Volume 2 | Issue 4 | 2018
Date of Publication : 2018-07-25
License: This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 01-12
Manuscript Number : IJSRCE182313
Publisher : Technoscience Academy
URL : https://ijsrce.com/IJSRCE182313
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