An effective early fraud detection method for online auctions

Electronic Commerce Research and Applications

Volumn 11, Issue 4, 2012, Pages 346-360

  Chang, Wen Hsi ^a   Chang, Jau Shien ^a  

^a TAMKANG UNIVERSITY   (Taiwan)

Author keywords

Classification; Electronic auctions; Electronic commerce; Fraud detection

Indexed keywords

DATA COLLECTION; DETECTION SYSTEM; ELECTRONIC AUCTION; FRAUD DETECTION; FRAUD DETECTION SYSTEM; FRAUDSTERS; INSTANCE BASED LEARNING; MODELING PROCEDURE; ONLINE AUCTIONS; PRO-ACTIVE APPROACH;

CLASSIFICATION (OF INFORMATION); COMPUTER CRIME; DECISION TREES; ELECTRONIC COMMERCE; PROFITABILITY;

CRIME;

EID: 84865241568     PISSN: 15674223     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.elerap.2012.02.005     Document Type: Article

References (59)

1. D. Aha, and D. Kibler Instance-based learning algorithms Machine Learning 6 1991 37 66
2. Balingit, R.; Trevathan, J.; Lee, Y. J.; and Read, W. A software tool for collecting data from online auctions. In Proceedings the 6th International Conference on Information Technology: New, Generations, 2009, 922-927.
3. A. Blum, and P. Langley Selection of relevant features and examples in machine learning Artificial Intelligence 97 1-2 1997 245 271
4. Buchegger, S.; and Boudec, J. A robust reputation system for mobile ad-hoc, networks. EPFL IC Technical Report, 2003.
5. Burge, P.; Shawe-Taylor, J.; Cooke, C.; Moreau, Y.; Preneel, B.; and Stoermann, C. Fraud detection and management in mobile telecommunications networks. In Proceedings of the European Conference on Security and Detection, April 28-30, 1997, 91-96.
6. P. Chan, W. Fan, A.L. Prodromidis, and S.J. Stolfo Distributed data mining in credit card fraud detection Intelligent Systems and their Applications 14 6 1999 67 74
7. Chang, J. S.; and Chang, W. H. An early fraud detection mechanism for online auctions based on phased modeling. In Proceedings of the 2009 International Workshop on Mobile Systems E-Commerce and Agent Technology, Taipei, Taiwan, December 3-5, 2009.
8. Chang, W. H.; and Chang, J. S. A multiple-phased modeling method to identify potential fraudsters in online auctions. In Proceedings of the 2nd International Conference on Computer Research and Development, Kuala Lumpur, Malaysia, May 7-10, 2010a.
9. Chang, W. H.; and Chang, J. S. Using clustering techniques to analyze fraudulent behavior changes in online auctions. In Proceedings of 2010 International Conference on Networking and Information Technology, Manila, Philippines, June 11-13, 2010b.
10. Chang, W. H.; and Chang, J. S. An online auction fraud screening mechanism for choosing trading partners. In Proceedings of the 2nd International Conference on Education Technology and Computer, Shanghai, China, June 22-24, 2010c.
11. W.H. Chang, and J.S. Chang A novel two-stage phased modeling framework for early fraud detection in online auctions Expert Systems with Applications 38 9 2011 11244 11260
12. Chau, D. H.; and Faloutsos, C. Fraud detection in electronic auctions. In Proceedings of European Web Mining Forum at ECML/PKDD 2005, Cavtat, Dubroknik, Croatia, September 22-26, 2003, October 3-7, 2005.
13. Chau, D. H.; Pandit, S.; and Faloutsos, C. Detecting fraudulent personalities in networks of online auctioneers. In Proceedings of Principles and Practices of Knowledge Discovery in Databases 2006, Lecture Notes in Computer Science 4213, Berlin, Germany, September 18-22, 2006, Springer, New York, NY, 2006a, 103-114.
14. Chau, D. Shashank, P.; and Faloutsos, C. Detecting fraudulent personalities in networks of online auctioneers. In Proceedings of the 17th European Conference on Machine Learning (ECML) and the 10th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD), Berlin, Germany, September 19-21, 2006b.
15. Chau, P. Catching bad guys with graph mining. XRDS: Crossroads, The ACM Magazine for Students 17, 3, 2011.
16. C.E.H. Chua, and J. Wareham Fighting Internet auction fraud: an assessment and proposal Computer 37 10 2004 31 37
17. S. Das Filters, Wrappers and a boosting-based hybrid for feature selection Proceedings of the 18th International Conference on Machine Learning, Williamstown, MA, June 28-July 1, 2001 2001 ACM Press New York, NY 47 81
18. M. Dash, and H. Liu Feature selection for classification Intelligent Data Analysis 1 1997 131 151
19. S. Dohono Early detection of insider trading in option markets Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Seattle, WA, August 22-25 2004 ACM Press New York, NY
20. eBay Inc. eBay 2009 annual report. San Jose, CA, 2010. Available at files.shareholder.com/downloads/ebay/923940436x0x361552/b45137ee-aa41-4c2c-94ca- d72d5b0844be/eBay-77655-BANNERLESS.pdf.
21. E. Frank, and I.H. Witten Generating accurate rule sets without global optimization J.W. Shavlik, Proceedings of the 15th International Conference on Machine Learning, Madison, WI, July 24-27, 1998 1998 Morgan Kaufmann Publishers San Francisco, CA 144 151
22. B. Gavish, and C. Tucci Reducing Internet auction fraud Communications of the ACM 51 5 2008 89 97
23. R. Gilad-Bachrach, A. Navot, and N. Tishby 2004. Margin based feature selection: Theory and algorithms Proceedings of the 21st International Conference on Machine Learning, Banff, Alberta, Canada, July 4-8 2004 ACM Press New York, NY 43 69
24. Ghosh, S.; and Reilly, D.L. Fraud detection with a neural-network. In Proceedings of the Twenty-Seventh Hawaii International Conference, January 4-7, 1994, 621-630.
25. P. Goes, Y. Tu, and A. Tung Online auctions hidden metrics Communications of the ACM 52 4 2009 147 149
26. M. Goodrich, and F. Kerschbaum Privacy-enhanced reputation-feedback methods to reduce feedback extortion in online auctions CODASPY '11: Proceedings of the First ACM Conference on Data and Application Security and Privacy, San Antonio, TX, February 7-9, 2011 2011 ACM Press New York, NY
27. D. Gregg, and J. Scott A typology of complaints about eBay sellers Communications of the ACM 51 4 2008 69 74
28. I. Guyon, and A. Elisseeff An introduction to variable and feature selection Journal of Machine Learning Research 3 2003 1157 1182
29. M.A. Hall, and A. Smith Feature selection for machine learning: Comparing a correlation-based filter approach to the wrapper Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference 1999 AAAI Press Menlo Park, CA
30. Institute for Information Industry. 2010 online market scale reaches 358.3 billion. Taipei, Taiwan, October 18, 2010. Available at mic.iii.org.tw/intelligence/pressroom/pop-pressfull.asp?sno=225&type1=2.
31. John, G.; Kohavi, R.; and Pfleger, P. Irrelevant features and the subset selection problem. In The Proceedings of the Eleventh International Conference on Machine Learning, Morgan Kaufmann, San Francisco, CA, 1994.
32. Kira, K.; and Rendell L. A. A practical approach to feature selection. in Proceeding of the 9th International Workshop on Machine Learning, Aberdeen, Scotland, 1992, Morgan Kaufmann, San Francisco, CA, 1992, 249-256.
33. R. Kohavi, and H.G. John Wrappers for feature subset selection Artificial Intelligence 97 1-2 1997 273 324
34. I. Kononenko Estimating attributes: Analysis and extensions of RELIEF F. Bergadano, L. De Raedt, Proceedings of the 1994 European Conference on Machine Learning Lecture Notes in Artificial Intelligence, Catania, Italy vol. 784 1994 Springer-Verlag Heidelberg, Germany 171 182
35. Y. Ku, Y. Chen, and C. Chiu A proposed data mining approach for Internet auction fraud detection Intelligence and Security Informatics, Lecture Notes in Computer Science vol. 4430 2007 Springer New York, NY 238 243
36. N. Kwak, and C.H. Choi Input feature selection for classification problems IEEE Transactions on Neural Networks 13 1 2002 143 159
37. M.H.C. Law, A.K. Jain, and A.T. Figueiredo Simultaneous feature selection and clustering using mixture models IEEE Transactions on Pattern Analysis and Machine Intelligence 26 9 2004 1 13
38. H. Liu, and R. Setiono Feature selection via discretization IEEE Transactions on Knowledge and Data Engineering 9 4 1997 642 645
39. H. Liu, and L. Yu Toward integrating feature selection algorithms for classification and clustering IEEE Transactions on Knowledge and Data Engineering 17 4 2005 491 502
40. R. Maranzato, A. Pereira, A. Lago, and M. Neubert Fraud detection in reputation systems in e-markets using logistic regression Proceedings of the 2010 ACM Symposium on Applied Computing, Sierre, Switzerland, March 22-26, 2010 2010 ACM Press New York, NY 14 26
41. R. Maranzato, A. Pereira, M. Neubert, and A. Lago Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization SIGAPP Applied Computing Review 11 2010 1
42. M. McGlohon, S. Bay, M. Anderle, D. Steier, and C. Faloutsos SNARE: A link analytic system for graph labeling and risk detection Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2009 ACM Press New York, NY 1265 1274
43. T.M. Michelle Machine Learning 1997 McGraw Hill New York, NY
44. NW3C. National white collar crime and the Federal Bureau Investigation: 2009 Internet crime report - January 1-December 31, 2010. Available at www.ic3.gov/media/annualreport/2009-IC3Report.pdf.
45. Pandit, S.; Chau, D. H.; Wang, S.; and Faloutsos, C. NetProbe: A fast and scalable system for fraud detection in online auction networks. In Proceedings of the 16th international Conference on the World Wide Web, Banff, Canada, May 8-12, 2007, 201-210.
46. P. Pavlou, and A. Dimoka The nature and role of feedback text comments in online marketplaces: implications for trust building, price premiums, and seller differentiation Information Systems Research 17 4 2006 392 414
47. H. Peng, F. Long, and C. Ding Feature selection based on mutual information: criteria of max-dependency, max-relevance, and min-redundancy IEEE Transactions on Pattern Analysis and Machine Intelligence 27 8 2005 1226 1238
48. J.R. Quinlan C4.5 Programs for Machine Learning 1993 Morgan Kaufmann San Mateo, CA
49. P. Resnick, R. Zeckhauser, E. Friedman, and K. Kuwabara Reputation systems Communications of the ACM 43 12 2000 45 48
50. M. Robnik-Sikonja, and I. Kononenko An adaptation of Relief for attribute estimation in regression D. Fisher, Proceedings of the 14th International Conference on Machine Learning, Nashville, TN 1997 Morgan Kaufmann San Francisco, CA 296 304
51. S. Rubin, M. Christodorescu, V. Ganapathy, J.T. Giffin, L. Louis Kruger, H. Wang, and N. Kidd An auctioning reputation system based on anomaly Proceedings of the 12th ACM Conference on Computer and Communications Security, Alexandria, VA, November 7-10, 2005 2005 ACM Press New York, NY
52. Z. Shen, and N. Sundaresan EBay: An e-commerce marketplace as a complex network Proceedings of the Fourth ACM International Conference on Web Search and Data Mining, Hong Kong, China, February 9-12, 2011 2011 ACM Press New York, NY
53. Wang, J.; and Chiu, C. Q. Detecting online auction inflated-reputation behaviors using social network analysis. In Proceedings of North American Association for Computational Social and Organizational Science 2005, Notre Dame, Indiana, June 26-28, 2005.
54. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2nd edition 2005 Morgan Kaufmann San Francisco, CA
55. I.H. Witten, E. Frank, and M. Hall Data Mining: Practical Machine Learning Tools and Techniques 3rd edition 2010 Morgan Kaufmann Burlington, MA
56. X. Wu, V. Kumar, J. Quinlan, J. Ghosh, Q. Yang, H. Motoda, G. McLachlan, A. Ng, B. Liu, P. Yu, Z. Zhou, M. Steinbach, D. Hand, and D. Steinberg Top 10 algorithms in data mining Knowledge Information Systems 14 2008 1 37
57. L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy Journal of Machine Learning Research 5 2004 1205 1224
58. B. Zhang, Y. Zhou, and C. Faloutsos Toward a comprehensive model in Internet auction fraud detection R. Sprague, Proceedings of the 41st Annual Hawaii International Conference on System Sciences, Waikoloa, HI 2008 IEEE Computer Society Press Washington, DC 1 9
59. N. Zhong, and J. Dong Using rough sets with heuristics for feature selection Journal of Intelligent Information Systems 16 2001 199 214