Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques

  • Swati Ganguly
  • Samira SadaouiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10868)


E-auctions have attracted serious fraud, such as Shill Bidding (SB), due to the large amount of money involved and anonymity of users. SB is difficult to detect given its similarity to normal bidding behavior. To this end, we develop an efficient SVM-based fraud classifier that enables auction companies to distinguish between legitimate and shill bidders. We introduce a robust approach to build offline the optimal SB classifier. To produce SB training data, we combine the hierarchical clustering and our own labelling strategy, and then utilize a hybrid data sampling method to solve the issue of highly imbalanced SB datasets. To avert financial loss in new auctions, the SB classifier is to be launched at the end of the bidding period and before auction finalization. Based on commercial auction data, we conduct experiments for offline and online SB detection. The classification results exhibit good detection accuracy and misclassification rate of shill bidders.


Data clustering Data labeling Data sampling Supervised learning SVM In-Auction Fraud Shill bidding Fraud detection 


  1. 1.
    Abedinzadeh, S., Sadaoui, S.: A rough sets-based agent trust management framework. Int. J. Intell. Syst. Appl. 5(4), 1–9 (2013)Google Scholar
  2. 2.
    Akbani, R., Kwek, S., Japkowicz, N.: Applying support vector machines to imbalanced datasets. In: Boulicaut, J.-F., Esposito, F., Giannotti, F., Pedreschi, D. (eds.) ECML 2004. LNCS (LNAI), vol. 3201, pp. 39–50. Springer, Heidelberg (2004). Scholar
  3. 3.
    Chawla, N.V., et al.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)Google Scholar
  4. 4.
    Chawla, N.V.: C4.5 and imbalanced data sets: investigating the effect of sampling method, probabilistic estimate, and decision tree structure. In: International Conference on Machine Learning (2003)Google Scholar
  5. 5.
    Dong, F., Shatz, S., Xu, H.: Combating online in-auction frauds: clues, techniques and challenges. Comput. Sci. Rev. 3(4), 245–258 (2009)CrossRefGoogle Scholar
  6. 6.
    Dong, F., Shatz, S.M., Xu, H., Majumdar, D.: Price comparison: a reliable approach to identifying SB in online auctions? Electron. Commer. Res. Appl. 11(2), 171–179 (2012)CrossRefGoogle Scholar
  7. 7.
    Ford, B.J., Haiping, X., Valova, I.: A real-time self-adaptive classifier for identifying suspicious bidders in online auctions. Comput. J. 56(5), 646–663 (2013)CrossRefGoogle Scholar
  8. 8.
    Ganguly, S., Sadaoui, S.: Classification of imbalanced auction fraud data. In: Mouhoub, M., Langlais, P. (eds.) AI 2017. LNCS (LNAI), vol. 10233, pp. 84–89. Springer, Cham (2017). Scholar
  9. 9.
    Gupta, P., Mundra, A.: Online in-auction fraud detection using online hybrid model. In: International Conference on Computing, Communication & Automation (2015)Google Scholar
  10. 10.
    He, H., Garcia, E.A.: Learning from imbalanced data. IEEE Trans. Knowl. Data Eng. 21(9), 1263–1284 (2009)CrossRefGoogle Scholar
  11. 11.
    Hernandez, J., Carrasco-Ochoa, J.A., Martínez-Trinidad, J.F.: An empirical study of oversampling and undersampling for instance selection methods on imbalance datasets. In: Ruiz-Shulcloper, J., Sanniti di Baja, G. (eds.) CIARP 2013. LNCS, vol. 8258, pp. 262–269. Springer, Heidelberg (2013). Scholar
  12. 12.
    Köknar-Tezel, S., Latecki, L.J.: Improving SVM classification on imbalanced data sets in distance spaces. In: IEEE International Conference on Data Mining, pp. 259–269 (2009)Google Scholar
  13. 13.
    Nikitkov, A., Bay, D.: Online auction fraud: ethical perspective. J. Bus. Ethics 79(3), 235–244 (2008)CrossRefGoogle Scholar
  14. 14.
    Nikitkov, A., Bay, D.: SB: empirical evidence of its effectiveness and likelihood of detection in online auction systems. Int. J. Account. Inf. Syst. 16, 42–54 (2015)CrossRefGoogle Scholar
  15. 15.
    Ochaeta, K.: Fraud Detection for Internet Auctions. “A Data Mining Approach”, Master’s Thesis, College of Technology Management, National Tsing-Hua University, Hsinchu, Taiwan (2008)Google Scholar
  16. 16.
    Phua, C., et al.: A comprehensive survey of data mining-based fraud detection research. arXiv preprint arXiv:1009.6119 (2010)
  17. 17.
    Resnick, P., et al.: Reputation systems. Commun. ACM 43(12), 45–48 (2000)Google Scholar
  18. 18.
    Sabau, A.S.: Survey of clustering based financial fraud detection research. Informatica Economica 16(1), 110 (2012)MathSciNetGoogle Scholar
  19. 19.
    Sadaoui, S., Wang, X.: A dynamic stage-based fraud monitoring framework of multiple live auctions. Appl. Intell. (2016). Scholar
  20. 20.
    Sallehuddin, R., Ibrahim, S., Elmi, A.H.: Classification of SIM box fraud detection using support vector machine and artificial neural network. Int. J. Innov. Comput. 4(2), 19–27 (2014)Google Scholar
  21. 21.
    Seiffert, C., et al.: An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Inf. Sci. 259, 571–595 (2014)Google Scholar
  22. 22.
    Trevathan, J., Read, W.: Detecting SB in online English auctions. In: Handbook of Research on Social and Organizational Liabilities in Information Security, p. 446 (2008)Google Scholar
  23. 23.
    Trevathan, J.: Getting into the mind of an “in-auction” fraud perpetrator. Comput. Sci. Rev. 27, 1–15 (2018)MathSciNetCrossRefGoogle Scholar
  24. 24.
    Weiss, G.M., McCarthy, K., Zabar, B.: Cost-sensitive learning vs. sampling: which is best for handling unbalanced classes with unequal error costs? In: International Conference on Data Mining, pp. 35–41 (2007)Google Scholar
  25. 25.
    Yoshida, T., Ohwada, H.: Shill bidder detection for online auctions. In: Zhang, B.-T., Orgun, M.A. (eds.) PRICAI 2010. LNCS (LNAI), vol. 6230, pp. 351–358. Springer, Heidelberg (2010). Scholar
  26. 26.
    Zhang, S., Sadaoui, S., Mouhoub, M.: An empirical analysis of imbalanced data classification. Comput. Inf. Sci. 8(1), 151 (2015)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.University of ReginaReginaCanada

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