Phishing detection project on PHP

PHP ideas     valar mathi    2015-09-04

10 Reviews


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Phishing detection project abstract

Posted by :  valar mathi
Project Name :  Phishing detection
Upload Date :  2015-09-04
Platform :  PHP
Programming Language :  html,css,php
Database :  mysql
Project Type :  desktop Application
View :  6463
PHP trending projects

Phishing detection project description

it is a web based application project. i need this short period. please.Abstract: Phishing is an e-mail fraud method in whichthe perpetrator sends out legitimate-looking email in anattempt to gather personal and financial informationfrom any online users. The hackers then steal this personalinformation for their own purposes, or sell theinformation to any other criminal parties. There are varioustechniques to detect phishing websites. However,Classification Data Mining (DM) Techniques can be avery useful tool in detecting and identifying e-bankingphishing websites. This paper proposes two algorithmsto overcome the difficulty and complexity in detectingand predicting e-banking phishing website . First approachknown as Multiclass Classification based onAssociation Rule (MCAR) based on using associationand classification Data Mining algorithms. The algorithmsused to characterize and identify all the factorsand rules in order to classify the phishing website andthe relationship that correlate them with each other. Anotherapproach called Adaptive Boosting (Adaboost) isalso a powerful classifier in which final classification isbased on vote of weak classifiers.This paper proposes the modification to originalMCAR algorithms with redundant rule pruning techniqueto reduce number of rules generated in final classifierand minimizes redundancy, hence improves theaccuracy. In this paper Adaboost classifier is first timeapplied to detect phishing website. Results are comparedwith respect to time, accuracy of MCAR andAdaboost. We also analysed MCAR for generated ruleto discarded rule with support and confidence values asa result of pruning techniques applied. The experimentalresult demonstrates the feasibility of using AssociativeClassification techniques in real applications and itsbetter performance improvement as compared to Adaboostand other classifications algorithms.Keywords : Phishing, Association, Classification, Pruning.MCAR, Adaptive Boosting.

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