Collusion Detection in Online Rating Systems

  • Mohammad Allahbakhsh
  • Aleksandar Ignjatovic
  • Boualem Benatallah
  • Seyed-Mehdi-Reza Beheshti
  • Elisa Bertino
  • Norman Foo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)


Online rating systems are subject to unfair evaluations. Users may try to individually or collaboratively promote or demote a product. Collaborative unfair rating, i.e., collusion, is more damaging than individual unfair rating. Detecting massive collusive attacks as well as honest looking intelligent attacks is still a real challenge for collusion detection systems. In this paper, we study impact of collusion in online rating systems and asses their susceptibility to collusion attacks. The proposed model uses frequent itemset mining technique to detect candidate collusion groups and sub-groups. Then, several indicators are used for identifying collusion groups and to estimate how damaging such colluding groups might be. The model has been implemented and we present results of experimental evaluation of our methodology.


Rating Score Damage Impact Reputation System Frequent Itemset Mining Reputation Score 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of VLDB 1994, pp. 487–499 (1994)Google Scholar
  2. 2.
    Allahbakhsh, M., Ignjatovic, A., Benatallah, B., Beheshti, S.-M.-R., Foo, N., Bertino, E.: Detecting, Representing and Querying Collusion in Online Rating Systems. ArXiv e-prints (November 2012)Google Scholar
  3. 3.
    Beheshti, S.-M.-R., Benatallah, B., Motahari-Nezhad, H.R., Allahbakhsh, M.: A framework and a language for on-line analytical processing on graphs. In: Wang, X.S., Cruz, I., Delis, A., Huang, G. (eds.) WISE 2012. LNCS, vol. 7651, pp. 213–227. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  4. 4.
    Ciccarelli, G., Cigno, R.L.: Collusion in peer-to-peer systems. Computer Networks 55(15), 3517–3532 (2011)CrossRefGoogle Scholar
  5. 5.
    Flanagin, A., Metzger, M., Pure, R., Markov, A.: User-generated ratings and the evaluation of credibility and product quality in ecommerce transactions. In: HICSS 2011, pp. 1–10. IEEE (2011)Google Scholar
  6. 6.
    Harmon, A.: Amazon glitch unmasks war of reviewers. NY Times (February 14, 2004)Google Scholar
  7. 7.
    Brown, J.M.J.: Reputation in online auctions: The market for trust. California Management Review 49(1), 61–81 (2006)CrossRefGoogle Scholar
  8. 8.
    Kamvar, S.D., Schlosser, M.T., Garcia-Molina, H.: The eigentrust algorithm for reputation management in p2p networks. In: Proceedings of the WWW 2003, pp. 640–651 (2003)Google Scholar
  9. 9.
    Kerr, R.: Coalition detection and identification. In: The 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 1657–1658 (2010)Google Scholar
  10. 10.
    Lee, H., Kim, J., Shin, K.: Simplified clique detection for collusion-resistant reputation management scheme in p2p networks. In: ISCIT 2010, pp. 273–278 (2010)Google Scholar
  11. 11.
    Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing, vol. 1. ACM, New York (2007)Google Scholar
  12. 12.
    Lim, E., et al.: Detecting product review spammers using rating behaviors. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM 2010, pp. 939–948. ACM, New York (2010)Google Scholar
  13. 13.
    Mukherjee, A., Liu, B., Glance, N.: Spotting fake reviewer groups in consumer reviews. In: Proceedings of the 21st International Conference on World Wide Web. ACM (2012)Google Scholar
  14. 14.
    Qio, L., et al.: An empirical study of collusion behavior in the maze p2p file-sharing system. In: Proceedings of the ICDCS 2007, p. 56 (2007)Google Scholar
  15. 15.
    Salton, G., Buckley, C., Fox, E.A.: Automatic query formulations in information retrieval. Journal of the American Society for Information Science 34(4), 262–280 (1983)CrossRefGoogle Scholar
  16. 16.
    Salton, G., McGill, M.: Introduction to modern information retrieval. McGraw-Hill computer science series. McGraw-Hill (1983)Google Scholar
  17. 17.
    Sun, Y., Liu, Y.: Security of online reputation systems: The evolution of attacks and defenses. IEEE Signal Processing Magazine 29(2), 87–97 (2012)CrossRefGoogle Scholar
  18. 18.
    Swamynathan, G., Almeroth, K., Zhao, B.: The design of a reliable reputation system. Electronic Commerce Research 10, 239–270 (2010), 10.1007/s10660-010-9064-yzbMATHCrossRefGoogle Scholar
  19. 19.
    Yang, Y., Feng, Q., Sun, Y.L., Dai, Y.: Reptrap: a novel attack on feedback-based reputation systems. In: Proceedings of SecureComm 2008, pp. 8:1–8:11 (2008)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Mohammad Allahbakhsh
    • 1
  • Aleksandar Ignjatovic
    • 1
  • Boualem Benatallah
    • 1
  • Seyed-Mehdi-Reza Beheshti
    • 1
  • Elisa Bertino
    • 2
  • Norman Foo
    • 1
  1. 1.The University of New South WalesSydneyAustralia
  2. 2.Department of Computer Science and CERIASPurdue UniversityUSA

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