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Job Resume TemplatePhishing detection project features and function requirement. Share PHP Project ideas and topics with us. Grate and many PHP project ideas and topics . Here some PHP project ideas for research paper. Here large collection of PHP project with source code and database. We many idea to development application like mobile application,desktop software application,web application development. You can find more project topics and ideas on PHP. Development ideas on Phishing detection. You can find Top Downloaded PHP projects here. Many project available to download with PHP source code and database. Free download Phishing detection project synopsis available.
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| Publish by | valar mathi |
| Project Name | Phishing detection |
| Upload Date | September 4, 2015 |
| Platform | PHP |
| Programming Language | html,css,php |
| Database | mysql |
| Front end | |
| Back end | |
| Project Type | desktop Application |
| View | 16125 |
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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