Abstract
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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of VLDB 1994, pp. 487–499 (1994)
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)
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)
Ciccarelli, G., Cigno, R.L.: Collusion in peer-to-peer systems. Computer Networks 55(15), 3517–3532 (2011)
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)
Harmon, A.: Amazon glitch unmasks war of reviewers. NY Times (February 14, 2004)
Brown, J.M.J.: Reputation in online auctions: The market for trust. California Management Review 49(1), 61–81 (2006)
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)
Kerr, R.: Coalition detection and identification. In: The 9th International Conference on Autonomous Agents and Multiagent Systems, pp. 1657–1658 (2010)
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)
Leskovec, J., Adamic, L.A., Huberman, B.A.: The dynamics of viral marketing, vol. 1. ACM, New York (2007)
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)
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)
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)
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)
Salton, G., McGill, M.: Introduction to modern information retrieval. McGraw-Hill computer science series. McGraw-Hill (1983)
Sun, Y., Liu, Y.: Security of online reputation systems: The evolution of attacks and defenses. IEEE Signal Processing Magazine 29(2), 87–97 (2012)
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-y
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Allahbakhsh, M., Ignjatovic, A., Benatallah, B., Beheshti, SMR., Bertino, E., Foo, N. (2013). Collusion Detection in Online Rating Systems. In: Ishikawa, Y., Li, J., Wang, W., Zhang, R., Zhang, W. (eds) Web Technologies and Applications. APWeb 2013. Lecture Notes in Computer Science, vol 7808. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37401-2_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-37401-2_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-37400-5
Online ISBN: 978-3-642-37401-2
eBook Packages: Computer ScienceComputer Science (R0)