Abstract
To combat online auction fraud, researchers have developed fraud detection and prevention methods. However, it is difficult to effectively evaluate these methods using commercial or synthetic auction data. For commercial data, it is not possible to accurately identify cases of fraud. For synthetic auction data, the conclusions drawn may not extend to the real world. The availability of realistic synthetic auction data, which models real auction data, will be invaluable for effective evaluation of fraud detection algorithms. We present an agent-based simulator that is capable of generating realistic English auction data. The agents and model are based on data collected from the TradeMe online auction site. We evaluate the generated data in two ways to show that it is similar to the TradeMe data. Evaluation of individual features show that correlation is greater than 0.9 for 8 of the 10 features, and evaluation using multiple features gives a median accuracy of 0.87.
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References
Scherer, K.: Retail store wars move online (2011)
Bhargava, B., Jenamani, M., Zhong, Y.: Counteracting shill bidding in online english auction. International Journal of Cooperative Information Systems 14 (2005)
Pandit, S., Chau, D.H., Wang, S., Faloutsos, C.: Netprobe: a fast and scalable system for fraud detection in online auction networks. In: Proceedings of the 16th International Conference on World Wide Web, pp. 201–210 (2007)
Chae, M., Shim, S., Cho, H., Lee, B.: An empirical analysis of fraud detection in online auctions: Credit card phantom transaction. In: Proceedings of the 40th Annual Hawaii International Conference on System Sciences, p. 155a. IEEE Computer Society
Chau, D., Pandit, S., Faloutsos, C.: Detecting Fraudulent Personalities in Networks of Online Auctioneers. In: Fürnkranz, J., Scheffer, T., Spiliopoulou, M. (eds.) PKDD 2006. LNCS (LNAI), vol. 4213, pp. 103–114. Springer, Heidelberg (2006)
Trevathan, J., Read, W.: Detecting collusive shill bidding. In: Fourth International Conference on Information Technology, ITNG 2007, pp. 799–808 (2007)
Shah, H., Joshi, N., Sureka, A., Wurman, P.: Mining eBay: Bidding Strategies and Shill Detection. In: Zaïane, O.R., Srivastava, J., Spiliopoulou, M., Masand, B. (eds.) WebKDD 2003. LNCS (LNAI), vol. 2703, pp. 17–34. Springer, Heidelberg (2003)
Macal, C.M., North, M.J.: Agent-based modeling and simulation. In: Proceedings of the Simulation Conference (WSC), pp. 86–98 (Winter 2009)
Mizuta, H., Steiglitz, K.: Agent-based simulation of dynamic online auctions. In: Simulation Conference Proceedings, vol. 2, pp. 1772–1777 (Winter 2000)
Bapna, R., Goes, P., Gupta, A.: Simulating online yankee auctions to optimize sellers revenue. In: Proceedings of the 34th Annual Hawaii International Conference on System Sciences, p. 10 (2001)
Menezes, F.M., Monteiro, P.K.: Auction Types. In: An Introduction to Auction Theory, pp. 10–11. Oxford University Press, USA (2005)
TradeMe (April 17, 2012), http://www.trademe.co.nz/
Lange, T., Roth, V., Braun, M.L., Buhmann, J.M.: Stability-based validation of clustering solutions. Neural Comput. 16(6), 1299–1323 (2004)
Myers, J.L., Well, A.D.: Research Design & Statistical Analysis, 2nd edn. Lawrence Erlbaum Associates (2003)
Haw-ren, F., Saad, Y.: Farthest centroids divisive clustering. In: Seventh International Conference on Machine Learning and Applications, ICMLA 2008, pp. 232–238 (2008)
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Tsang, S., Dobbie, G., Koh, Y.S. (2012). Generating Realistic Online Auction Data. In: Thielscher, M., Zhang, D. (eds) AI 2012: Advances in Artificial Intelligence. AI 2012. Lecture Notes in Computer Science(), vol 7691. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35101-3_11
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DOI: https://doi.org/10.1007/978-3-642-35101-3_11
Publisher Name: Springer, Berlin, Heidelberg
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