vioft2nntf2t|tblJournal|Abstract_paper|0xf4ff34952b0000000bb8070001000500 Fraud means accessing of information by unauthorized users. In today’s scenario, online fraud plays a vital role in many internet applications. Web mining is the application of data mining which detect and extract information from web documents using mining techniques. These techniques also determine the behaviour of the user, i.e. authorized or unauthorized. For the detection of fraud, fraud detection systems are used. Credit card fraud is increase day by day. In this paper, we used phishing tank database to detect emerging fraud using clustering, classification and regression techniques. We collect the data from database and apply pre-processing techniques to remove irrelevant data and proposed architecture and approach to detect fraud and find the following metrics i.e. accuracy, error rate, memory consumption and search time.
Preeti Rathi, Nipur Singh
Kanya Gurukul Campus Dehradun, India
Emerging Fraud, Web Mining, Clustering, Classification, Association Rule Mining, Pop-Up Windows
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| Published By :
ICTACT
Published In :
ICTACT Journal on Soft Computing
( Volume: 11 , Issue: 1 , Pages: 2195-2198 )
Date of Publication :
October 2020
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100
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