Collusion Detection in Online Rating Systems
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.
Unable to display preview. Download preview PDF.
- 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.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
- 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.Harmon, A.: Amazon glitch unmasks war of reviewers. NY Times (February 14, 2004)Google Scholar
- 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.Kerr, R.: Coalition detection and identification. In: The 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 1657–1658 (2010)Google Scholar
- 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.Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing, vol. 1. ACM, New York (2007)Google Scholar
- 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.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.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
- 16.Salton, G., McGill, M.: Introduction to modern information retrieval. McGraw-Hill computer science series. McGraw-Hill (1983)Google Scholar
- 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