Two-stage credit card fraud detection using sequence alignment

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4332 LNCS, Issue , 2006, Pages 260-275

  Kundu, Amlan ^a   Sural, Shamik ^a   Majumdar, A K ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

[No Author keywords available]

Indexed keywords

ANOMALY DETECTION; CRIME; INFORMATION SYSTEMS; INFORMATION USE; PATTERN MATCHING; STOCHASTIC SYSTEMS;

CREDIT CARD FRAUD DETECTIONS; CREDIT CARD TRANSACTIONS; FRAUD DETECTION SYSTEM; FRAUDULENT TRANSACTIONS; HYBRID APPROACH; MISUSE DETECTION; PATTERN-MATCHING TECHNIQUE; SEQUENCE ALIGNMENTS;

STOCHASTIC MODELS;

EID: 85017038101     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11961635_18     Document Type: Conference Paper

References (16)

1. http://www2.acnielsen.com/reports/documents/2005 cc onlineshopping.pdf, Global Consumer Attitude Towards Online Shopping, 28 Apr 2006.
2. http://www.epaynews.com/statistics/fraud.html, Statistics for General and Online Card Fraud, 22 Apr 2006.
3. http://news.bbc.co.uk/2/hi/business/3256799.stm Business news on Preventing card fraud BBC NEWS, 8 Mar 2005.
4. S. Ghosh, D.L. Reilly, Credit Card Fraud Detection with a Neural-Network, Proceedings of the Annual International Conference on System Science, Pages 621-630, 1994.
5. S. J. Stolfo, D.W. Fan, W. Lee, A.L. Prodromidis, Credit Card Fraud Detection Using Meta-Learning: Issues and Initial Results, Proceedings of the Workshop on AI Methods in Fraud and Risk Management, 1997.
6. E. Aleskerov, B. Freisleben, B. Rao, CARDWATCH: A Neural Network Based Database Mining System for Credit Card Fraud Detection, Proceedings of the IEEE/IAFE, Pages 220-226, 1997.
7. W. Fan, A.L. Prodromidis, S.J. Stolfo, Distributed Data Mining in Credit Card Fraud Detection, Proceedings of IEEE Intelligent Systems, Pages 67-74, 1999.
8. R. Brause, T. Langsdorf, M. Hepp, Neural Data Mining for Credit Card Fraud Detection, International Conference on Tools with Articial Intelligence, Pages 103-106, 1999.
9. C. Chiu, C. Tsai, A Web Services-Based Collaborative Scheme for Credit Card Fraud Detection, Proceedings of the IEEE International Conference on e-Technology, e-Commerce and e-Service, Pages 177-181, 2004.
10. C. Phua, V. Lee, K. Smith, R. Gayler, A Comprehensive Survey of Data Mining-based Fraud Detection Research, Proceedings of the Articial Intelligence, 2005.
11. V. Vatsa, S. Sural, A.K. Majumdar, A Game- Theoretic Approach to Credit Card Fraud Detection, Lecture Notes in Computer Science, Springer Verlag, Proceedings of International Conference on Information Systems Security, Pages 263-276, 2005.
12. K. Takeda, The application of bioinformatics to network intrusion detection, Security Technology, 39th Annual International Conference (CCST) IEEE, Pages 130-132, 2005.
13. S. Coul, J.Branch, B. Szymanski, E. Breimer, Intrusion Detection: A Bioinformatics Approach, Proceedings of the 19th Annual Computer Security Applications Conference (ACSAC) IEEE, Pages 24-33, 2003.
14. S.B. Needleman, C.D. Wunsch, A general method applicable to the search for similarities in the amino acid sequence of two proteins. Journal of Molecular Biology, Pages 443-453, 1970.
15. T. F. Smith, M.S. Waterman, Identification of common molecular subsequences. Journal of Molecular Biology, Pages 195-197, 1981.
16. S. F. Altschul, W. Gish, W. Miller, W. Myers, J. Lipman, Basic Local Alignment Search Tool, Journal of Molecular Biology, Pages 403-410, 1990.