Data mining approach for subscription-fraud detection in telecommunication sector

Contemporary Engineering Sciences

Volumn 7, Issue 9-12, 2014, Pages 515-522

  Saravanan, P ^a   Subramaniyaswamy, V ^a   Sivaramakrishnan, N ^a   Arun Prakash, M ^a   Arunkumar, T ^b  

^a SASTRA UNIVERSITY   (India)

^b VIT UNIVERSITY   (India)

Author keywords

Fraud detection; K L divergence; Naive Bayes classification; Telecommunication

Indexed keywords

EID: 84904396148     PISSN: 13136569     EISSN: 13147641     Source Type: Journal    
DOI: 10.12988/ces.2014.4431     Document Type: Article

References (6)

1. D.Olszewski, A probabilistic approach to fraud detection in telecommunications, Knowledge Based Systems, Volume 26, pp. 246-258, 2012.
2. H.Farvaresh, M.M.Sepehri. "A data mining framework for detecting subscription fraud in telecommunication", Engineering applications of artificial intelligence, Volume 24, No. 1 pp. 182-194, 2011.
3. R.J. Bolton, David, Statistical fraud detection: A review, Statistical Science, Volume 17, pp. 235-255, 2002.
4. Y.Kou, C.-T.Lu, S.Sinvongwattana, Y.-P.Huang, Survey of fraud detection techniques, Proceedings of the IEEE International Conference on Networking, Sensing & Control, Volume 2, pp. 749-754, 2004.
5. P.Burge, J.Shawe-Taylor, C.Cooke, Y.Moreau, B.Preneel, C.Stoermann, Fraud detection and management in mobile telecommunications networks, Proceedings of the European Conference on Security and Detection ECOS97, pp.91-96, 1997.
6. T.Fawcett, F.Provost, Adaptive fraud detection, Data Mining and Knowledge Discovery, Volume 1, No. 3, pp. 291-316, 1997.