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Introduction on Content Based Phishing Detection

Author(s):

Shweta Dudhat , SIGMA INSTITUTE OF ENGGINEERING; Priyank Patel, SIE; Nayan Mali, SIE; Romil Patel, SIE

Keywords:

phishing detection, classification, data mining, security, MCAC algorithm

Abstract

Website phishing is the threatening challenge for the online society due to large number of transactions over the internet which happens on daily bases. Phishing tries to attempt to gather sensitive information by masquerading as a trustworthy entity in an electronic transaction/communication. The social networking sites like Facebook, Twitter and E-mails accounts are more affected from phishing or fake pages. The main idea behind writing this is to investigate the use of automated data mining ways in finding the complex problems of finding phishing websites for helping the users from being hacked. The approach for data mining is called Associative Classification method that suites best for finding phishing websites accurately. The common associative classification algorithm MCAC: “Multi-Label Classifiers based Associative Classification” to seek its applicability to the phishing. MCAC detects phishing websites with high accuracy than other algorithms and it generates hidden rules that other algorithms are unable to find and has improved predictive performance

Other Details

Paper ID: IJSRDV3I120459
Published in: Volume : 3, Issue : 12
Publication Date: 01/03/2016
Page(s): 392-394

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