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This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTINGREFERENCES1. http://www2.acnielsen.com/reports/documents/2005_cc_onlineshopping.pdf, Global ConsumerAttitude Towards On-line Shopping, 07 March 2007.2. D.J. Hand, G. Blunt, M.G. Kelly, and N.M. Adams, “Data Mining for Fun and Profit,” StatisticalScience, vol. 15, no. 2, pp. 111–131, 2000.3. http://www.epaynews.com/statistics/fraud.html, Statistics for General and On-line Card Fraud, 07March 2007.4. S. Ghosh and D.L. Reilly, “Credit Card Fraud Detection with a Neural-Network,” Proc. 27 th HawaiiInternational Conference on System Sciences: Information Systems: Decision Support andKnowledge-Based Systems, vol. 3, pp. 621-630, 1994.5. M. Syeda, Y. Q. Zhang, and Y. Pan, “Parallel Granular Networks for Fast Credit Card FraudDetection,” Proc. IEEE International Conference on Fuzzy Systems, pp. 572-577, 2002.6. S.J. Stolfo, D.W. Fan, W. Lee, A.L. Prodromidis, and P.K. Chan, “Credit Card Fraud Detection usingMeta-Learning: Issues and Initial Results,” Proc. AAAI Workshop on AI Methods in Fraud and RiskManagement, pp. 83-90, 1997.7. S.J. Stolfo, D.W. Fan, W. Lee, A. Prodromidis, and P.K. Chan, “Cost-based Modeling for Fraud andIntrusion Detection: Results from the JAM Project,” Proc. DARPA Information SurvivabilityConference and Exposition, vol. 2, pp. 130-144, 2000.8. E. Aleskerov, B. Freisleben, and B. Rao, “CARDWATCH: A Neural Network Based DatabaseMining System for Credit Card Fraud Detection,” Proc. IEEE/IAFE: Computational Intelligence forFinancial Engineering, pp. 220-226, 1997.9. M.J. Kim and T.S. Kim, “A Neural Classifier with Fraud Density Map for Effective Credit CardFraud Detection,” Proc. International Conference on Intelligent Data Engineering and AutomatedLearning, Lecture Notes in Computer Science, Springer Verlag, no. 2412, pp. 378-383, 2002.31
This article has been accepted for publication in a future issue of this journal, but has not been fully edited. Content may change prior to final publication.IEEE TRANSACTIONS ON DEPEDABLE AND SECURE COMPUTING10. W. Fan, A.L. Prodromidis, and S.J. Stolfo, “Distributed Data Mining in Credit Card FraudDetection,” IEEE Intelligent Systems, vol. 14, no. 6, pp. 67-74, 1999.11. R. Brause, T. Langsdorf, and M. Hepp, “Neural Data Mining for Credit Card Fraud Detection,” Proc.IEEE International Conference on Tools with Artificial Intelligence, pp. 103-106, 1999.12. C. Chiu and C. Tsai, “A Web Services-based Collaborative Scheme for Credit Card Fraud Detection,”Proc. IEEE International Conference on e-Technology, e-Commerce and e-Service, pp. 177-181,2004.13. C. Phua, V. Lee, K. Smith, and R. Gayler, “A Comprehensive Survey of Data Mining-based FraudDetection Research,” available on-line at http://www.bsys.monash.edu.au/people/cphua/, 07 March2007.14. S. Stolfo and A.L. Prodromidis, “Agent-based Distributed Learning applied to Fraud Detection,”Technical Report, CUCS-014-99, Columbia University, USA, 1999.15. C. Phua D. Alahakoon, and V. Lee, “Minority Report in Fraud Detection: Classification of SkewedData,” ACM SIGKDD Explorations Newsletter, vol. 6, no. 1, pp. 50-59, 2004.16. V. Vatsa, S. Sural, and A.K. Majumdar, “A Game-theoretic Approach to Credit Card FraudDetection,” Proc. 1 st International Conference on Information Systems Security, Lecture Notes inComputer Science, Springer Verlag, pp. 263-276, 2005.17. S. Axelsson, “The Base-Rate Fallacy and the Difficulty of Intrusion Detection,” ACM Transactionson Information and System Security, vol. 3, no. 3, pp. 186-205, 2000.18. L.R. Rabiner, “A Tutorial on Hidden Markov Models and Selected Applications in SpeechRecognition,” Proceedings of the IEEE, vol. 77, no. 2, pp. 257-286, 1989.19. S.S. Joshi and V.V. Phoha, “Investigating Hidden Markov Models Capabilities in AnomalyDetection,” Proc. of the 43 rd ACM Annual Southeast Regional Conference, vol. 1, pp. 98–103, 2005.20. S.B. Cho and H.J. Park, “Efficient Anomaly Detection by Modeling Privilege Flows using HiddenMarkov Model,” Computer and Security, vol. 22, no. 1, pp. 45-55, 2003.32
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