Analysis of fraudulent behavior strategies in online auctions for detecting latent fraudsters

Electronic Commerce Research and Applications

Volumn 13, Issue 2, 2014, Pages 79-97

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

^a TAMKANG UNIVERSITY   (Taiwan)

Author keywords

Behavior fluctuation; Clustering; E commerce; Early fraud detection; Online auction

Indexed keywords

CLUSTER ANALYSIS; CRIME;

BEHAVIOR FLUCTUATION; BEHAVIOR STRATEGY; CLUSTERING; DETECTION ACCURACY; FRAUD DETECTION; MONITORING METHODS; ONLINE AUCTIONS; TRANSACTION HISTORY;

ELECTRONIC COMMERCE;

EID: 84897914197     PISSN: 15674223     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.elerap.2013.10.004     Document Type: Article

References (50)

1. Agrawal, R.; Srikant, R. Mining sequential patterns: generalizations and performance improvements. In The 5th Int. Conf. Extending Database Technology, Springer, Amsterdam, Holland, 1996, 3-17.
2. V. Chandola, A. Banerjee, and V. Kumar Anomaly detection: a survey ACM Computing Surveys 41 3 2009 11 58 (July)
3. Chang, J.; Chang, W. An early fraud detection mechanism for online auctions based on phased modeling. In 2009 Joint Conferences on Pervasive Computing (JCPC), Taipei, Taiwan, 2009, 743-748.
4. Chang, W.; Chang, J. A multiple-phased modeling method to identify potential fraudsters in online auctions. In The 2nd International Conference on Computer Research and Development, Kuala Lumpur, Malaysia, 2010, 186-190.
5. W. Chang, and J. Chang A novel two-stage phased modeling framework for early fraud detection in online auctions Expert System with Applications 38 9 2011 11244 11260
6. W. Chang, and J. Chang An effective early fraud detection method for online auctions Electronic Commerce Research and Applications 11 4 2012 346 360 (1 July)
7. D.H. Chau, and C. Faloutsos Fraud detection in electronic auction 2005 European Web Mining Forum at ECML/PKDD Porto, Portugal
8. C.E.H. Chua, and J. Wareham Fighting Internet auction fraud: an assessment and proposal Computer 37 1 2004 31 37 (October)
9. M. Dash, and H. Liu Feature selection for classification Intelligent Data Analysis 1 1997 131 156
10. eBay Inc. How feedback works, 1995. Available at http://pages.ebay.com/ help/feedback/howitworks.html. Last accessed on August 11, 2012.
11. eBay Inc. 2011 Annual report/form 10-K, 2011. Available at http://files.shareholder.com/downloads/ebay/1903545909x0xS1065088-12-6/1065088/ filing.pdf. Last accessed on June 1, 2012.
12. B. Gavish, and C. Tucci Reducing Internet auction fraud Communications of the ACM 51 5 2008 89 97 (May)
13. P. Goes, Y. Tu, and A. Tung Online auctions hidden metrics Communications of the ACM 52 4 2009 147 149
14. M.A. Hall, and A. Smith Feature Selection for Machine Learning: Comparing a Correlation-based Filter Approach to the Wrapper 1999 AAAI Press Palo Alto, California 235 239
15. J. Han, and M. Kamber Data Mining: Concepts and Techniques 2nd edition 2007 Morgan Kaufmann San Francisco, CA
16. K. Hoffman, D. Zage, and C. Nita-Rotaru A survey of attack and defense techniques for reputation systems ACM Computing Surveys 42 1 2009 1 31 (December)
17. Ishioka, T. Extended k-means with an efficient estimation of the number of clusters. In The 2nd International-Conference on Intelligent Data Engineering and Automated Learning, Hong Kong, China, 2000, 17-22.
18. Ishioka, T. An expansion of x-means for automatically determining the optimal number of clusters-progressive iterations of k-means and merging of the clusters. In The fourth IASTED International Conference on Computational Intelligence, Calgery, Alberta, Canada, July 4-6, 2005, 91-96.
19. Jøsang, A. Robustness of trust and reputation systems: does it matter? In The 6th IFIPTM International Conference on Trust Management, Surat, India, 2012, 253-262.
20. Jøsang, A.; Golbeck, J. Challenges for robust of trust and reputation systems. In The 5th International Workshop on Security and Trust Management, Saint Malo, 2009, 1-12.
21. A. Karahoca, D. Karahoca, and O. Kaya Data Mining to Cluster Human Performance by using Online Self Regulating Clustering Method 2008 World Scientific and Engineering Academy and Society (WSEAS) Stevens Point, Wisconsin, USA 198 203 Istanbul, Turkey
22. R. Kass, and L. Wasserman A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion Journal of the American Statistical Association 90 1995 773 775
23. T. Kaszuba, A. Hupa, and A. Wierzbicki Advanced feedback management for Internet auction reputation systems IEEE Internet Computing 14 5 2010 31 37 10.1109/MIC.2010.85 (September-October)
24. M. Kobayashi, and T. Ito A Transactional Relationship Visualization System in Internet Auctions 2007 IEEE Computer Society Washington, DC, USA 248-251
25. Kobayashi, M.; Ito, T. An approach to implement a trading network visualization system for internet auctions. In Proceedings of the Second International Conference on Knowledge, Information and Creativity Support Systems, JAIST Press, Ishikawa, Japan, 2007b.
26. M. Kobayashi, and T. Ito A transactional relationship visualization system in Internet auctions Electronic Commerce - Studies in Computational Intelligence 110 2008 87 99
27. P. Kumar, and S. Wasan Comparative analysis of k-mean based algorithms International Journal of Computer Science and Network Security 10 4 2010 314 318 (April)
28. 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 4430 2007 238 243
29. X.J. Liu, and Z. Zhou Exploratory Under-sampling for Class-imbalance Learning 2006 IEEE Computer Society Washington DC, USA 965-969. Hong Kong, China
30. J. MacQueen Some Methods for Classification and Analysis of Multivariate Observations 1967 University of California Press Berkeley, California 281-297
31. T.M. Michelle Machine Learning International edition 1997 McGraw-Hill Singapore
32. F. Moser, R. Ge, and M. Ester Joint Cluster Analysis of Attribute and Relationship Data without A-Priori Specification of the Number of Clusters 2007 ACM New York, NY, USA 510-519
33. NW3C. 2008 Internet crime report, 2009. Available at http://www.ic3.gov/ media/annualreport/2008-IC3Report.pdf. Last accessed on August 11, 2012.
34. NW3C. 2009 Internet crime report, 2010. Available at http://www.ic3.gov/ media/annualreport/2009-IC3Report.pdf. Last accessed on March 1, 2011.
35. NW3C. 2010 Internet crime report, 2011. http://www.ic3.gov/media/ annualreport/2010-IC3Report.pdf. Last accessed on August 2012.
36. NW3C. 2011 Internet crime report, 2012. Available at http://www.ic3.gov/ media/annualreport/2011-IC3Report.pdf. Last accessed on August 2012.
37. M. Panda, and M. Patra A novel classification via clustering method for anomaly based network intrusion detection system International Journal of Recent Trends in Engineering 2 1 2009 1 6 (November)
38. S. Pandit, D. Chau, S. Wang, and C. Faloutsos Netprobe: A Fast and Scalable System for Fraud Detection in Online Auction Networks 2007 ACM New York, NY, US 201-210
39. 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 (December)
40. D. Pelleg, and A. Moore P. Langley, X-Means: Extending k-Means with Efficient Estimation of the Number of Clusters 2000 Morgan Kaufmann Publishers San Francisco, CA 727 734
41. 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
42. J.R. Quinlan C4.5 Programs for Machine Learning 1993 Morgan Kaufmann San Mateo, CA
43. Sinapov, J.; Stoytchev, A. Detecting the functional similarities between tools using a hierarchical representation of outcomes. In 7th IEEE International Conference on Development and Learning, Monterey, CA, August 9-12, 2008, 91-96.
44. Vaucher, S.; Sahraoui, H.; Vaucher, J. Discovering new change patterns in object-oriented systems. In WCRE '08, 15th Working Conference, Montreal, QC, Canada, 2008, 37-41.
45. Wang, J.; Chiu, C. Detecting online auction inflated-reputation behaviors using social network analysis. In North American Association for Computational Social and Organizational Science Conference, Notre Dame, Indiana, USA, 2005.
46. I.H. Witten, E. Farank, and A. Hall Data Mining: Practical Machine Learning Tools and Techniques 3rd edition 2009 Morgan Kaufmann Publishers Burlington, MA
47. I.H. Witten, and E. Frank Data Mining: Practical Machine Learning Tools and Techniques 2nd edition 2005 Morgan Kaufmann San Francisco
48. Z. Xing, and E. Stroulia Understanding Class Evolution in Object-oriented Software 2004 IEEE Computer Society Los Alamitos, CA, USA 34-43
49. L. Yu, and H. Liu Efficient feature selection via analysis of relevance and redundancy Journal of Machine Learning Research 5 2004 1205 1224
50. N. Zhong, and J. Dong Using rough sets with heuristics for feature selection Journal of Intelligent Information Systems 16 3 2001 199 214 (August)