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Real-Time Collusive Shill Bidding Detection in Online Auctions

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AI 2018: Advances in Artificial Intelligence (AI 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11320))

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Abstract

Shill bidding is where a seller introduces fake bids into an auction to artificially inflate an item’s final price, thereby cheating legitimate bidders. Shill bidding detection becomes more difficult when a seller involves multiple collaborating shill bidders. Colluding shill bidders can distribute the work evenly among each other to collectively reduce their chances of being detected. Previous detection methods wait until an auction ends before determining who the shill bidders are. However, if colluding shill bidders are not detected during the auction, an honest bidder can potentially be cheated by the end of the auction. This paper presents a real-time collusive shill bidding detection algorithm for identifying colluding shill bidders while an auction is running. Experimental results on auction data show that the algorithm can potentially highlight colluding shill bidders in real-time.

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References

  1. Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: LOF: identifying density-based local outliers. ACM Sigmod Rec. 29(2), 93–104 (2000)

    Article  Google Scholar 

  2. Chau, D.H., 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). https://doi.org/10.1007/11871637_14

    Chapter  Google Scholar 

  3. Ford, B.J., Xu, H., Valova, I.: A real-time self-adaptive classifier for identifying suspicious bidders in online auctions. J. Comput. 56(5), 646–663 (2013)

    Article  Google Scholar 

  4. Majadi, N., Trevathan, J.: A real-time detection algorithm for identifying shill bidders in multiple online auctions. In: Proceedings of the 51st Hawaii International Conference on System Sciences, pp. 3831–3840. IEEE Press, USA (2018)

    Google Scholar 

  5. Majadi, N., Trevathan, J., Gray, H.: A run-time algorithm for detecting shill bidding in online auctions. J. Theor. Appl. Electron. Commer. Res. 13(3), 17–49 (2018)

    Article  Google Scholar 

  6. Sadaoui, S., Wang, X.: A dynamic stage-based fraud monitoring framework of multiple live auctions. Appl. Intell. 46(1), 1–17 (2016)

    Google Scholar 

  7. Tsang, S., Koh, Y.S., Dobbie, G., Alam, S.: SPAN: finding collaborative frauds in online auctions. Knowl.-Based Syst. 71, 389–408 (2014)

    Article  Google Scholar 

  8. Trevathan, J., Read, W.: Detecting shill bidding in online english auctions. In: Handbook of Research on Social and Organizational Liabilities in Information Security, vol. 46, pp. 446–470. IGI Press (2009)

    Google Scholar 

  9. Trevathan, J.: Getting into the mind of an in-auction fraud perpetrator. Comput. Sci. Rev. 27, 1–15 (2018)

    Article  MathSciNet  Google Scholar 

  10. Trevathan, J., Read, W.: A simple shill bidding agent. In: Proceedings of the 4th International Conference on Information Technology-New Generations, pp. 933–937. IEEE Press, USA (2007)

    Google Scholar 

  11. Trevathan, J., Read, W.: Investigating shill bidding behaviour involving colluding bidders. J. Comput. 2(10), 63–75 (2007)

    Article  Google Scholar 

  12. Xu, H., Cheng, Y.T.: Model checking bidding behaviors in internet concurrent auctions. Int. J. Comput. Syst. Sci. Eng. 22(4), 179–191 (2007)

    Google Scholar 

  13. Xu, H., Bates, C.K., Shatz, S.M.: Real-time model checking for shill detection in live online auctions. In: Proceedings of International Conference on Software Engineering Research and Practice, pp. 351–358. CSREA press, USA (2009)

    Google Scholar 

  14. Zhang, B., Zhou, Y., Faloutsos, C.: Toward a comprehensive model in internet auction fraud detection. In: Proceedings of the 41st Hawaii International Conference on System Sciences, pp. 79–88. IEEE Press, USA (2008)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Griffith University, Brisbane, QLD, 4111, Australia

    Nazia Majadi & Jarrod Trevathan

  2. University of Queensland, Brisbane, QLD, 4072, Australia

    Neil Bergmann

Authors
  1. Nazia Majadi
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  2. Jarrod Trevathan
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  3. Neil Bergmann
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Corresponding author

Correspondence to Nazia Majadi .

Editor information

Editors and Affiliations

  1. University of Canterbury, Christchurch, New Zealand

    Tanja Mitrovic

  2. School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand

    Bing Xue

  3. RMIT University, Melbourne, VIC, Australia

    Xiaodong Li

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Majadi, N., Trevathan, J., Bergmann, N. (2018). Real-Time Collusive Shill Bidding Detection in Online Auctions. In: Mitrovic, T., Xue, B., Li, X. (eds) AI 2018: Advances in Artificial Intelligence. AI 2018. Lecture Notes in Computer Science(), vol 11320. Springer, Cham. https://doi.org/10.1007/978-3-030-03991-2_19

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  • DOI: https://doi.org/10.1007/978-3-030-03991-2_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-03990-5

  • Online ISBN: 978-3-030-03991-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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