Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Novel Approach To Detection Of Emerging Fraud Using Mining Techniques


Affiliations
1 Department of Computer Science, Kanya Gurukul Campus Dehradun, India
     

   Subscribe/Renew Journal


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

Keywords

Emerging Fraud, Web Mining, Clustering, Classification, Association Rule Mining, Pop-Up Windows.
Subscription Login to verify subscription
User
Notifications
Font Size

Abstract Views: 5

PDF Views: 0




  • A Novel Approach To Detection Of Emerging Fraud Using Mining Techniques

Abstract Views: 5  |  PDF Views: 0

Authors

Preeti Rathi
Department of Computer Science, Kanya Gurukul Campus Dehradun, India
Nipur Singh
Department of Computer Science, Kanya Gurukul Campus Dehradun, India

Abstract


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

Keywords


Emerging Fraud, Web Mining, Clustering, Classification, Association Rule Mining, Pop-Up Windows.