Advertisement

Exploiting Trust and Suspicion for Real-time Attack Recognition in Recommender Applications

  • Ebrahim Bagheri
  • Ali A. Ghorbani
Part of the IFIP International Federation for Information Processing book series (IFIPAICT, volume 238)

Abstract

As is widely practiced in real world societies, fraud and deception are also ubiquitous in the virtual world. Tracking and detecting such malicious activities in the cyber space is much more challenging due to veiled identities and imperfect knowledge of the environment. Recommender systems are one of the most attractive applications widely used for helping users find their interests from a wide range of interesting choices that makes them highly vulnerable to malicious attacks. In this paper we propose a three dimensional trust based filtering model that detects noise and attacks on recommender systems through calculating three major factors: Importance, Frequency, and Quality. The results obtained from our experiments show that the proposed approach is capable of correctly detecting noise and attack and is hence able to decrease the absolute error of the predicted item rating value.

Keywords

Recommender System ARMA Model Collaborative Filter Reputation System Recommender Algorithm 
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.

Reference

  1. 1.
    Rashid, A.M., Karypis, G., and Riedl, J., Influence in Ratings-Based Recommender Systems: An Algorithm-Independent Approach. SIAM International Conference on Data Mining, 2005.Google Scholar
  2. 2.
    Golbeck, J., Hendler, J.A., Accuracy of Metrics for Inferring Trust and Reputation in Semantic Web-Based Social Networks. EKAW 2004.Google Scholar
  3. 3.
    Johnson, S., Keep Your Kids Safe on the Internet, McGraw-Hill Osbome Media, 2004.Google Scholar
  4. 4.
    Sun, Z., Finnie, G., “Experience Based Reasoning for Recognising Fraud and Deception,” Fourth International Conference on Hybrid Intelligent Systems (HIS’04), 2004.Google Scholar
  5. 5.
    Cristiano Castelfranchi, Yao-Hua Tan, The Role of Trust and Deception in Virtual Societies, International Journal of Electronic Commerce, Volume 6, Number 3 / Spring 2002.Google Scholar
  6. 6.
    Schillo, M., and Funk, P., Who can you trust: Dealing with deception. In Proceedings of the Autonomous Agents Workshop on Deception, Fraud and Trust in Agent Societies, 1999.Google Scholar
  7. 7.
    Zhao, S., Jiang, G., Huang, T., Yang, X., “The Deception Detection and Restraint in Multi-agent System,” 17th IEEE International Conference on Tools with Artificial Intelligence (ICTAI’05), 2005.Google Scholar
  8. 8.
    Bitting, E., Ghorbani A., Protecting e-commerce agents from defamation. Electronic Commerce Research and Applications 3(1): 21–38, 2004.CrossRefGoogle Scholar
  9. 9.
    Schafer, J. B., Konstan, J., and Riedi, J., Recommender systems in e-commerce. In Proceedings of the 1st ACM Conference on Electronic Commerce, 1999.Google Scholar
  10. 10.
    Massa, P., and Avesani, P., Trust-aware collaborative filtering for recommender systems. To Appear in: Proceedings of International Conference on Cooperative Information Systems, 2004.Google Scholar
  11. 11.
    Goldberg, L., Roeder, T., Gupta, D., Perkins, C., Eigentaste: A Constant Time Collaborative Filtering Algorithm, Information Retrieval, Volume 4, Issue 2, Jul 2001.Google Scholar
  12. 12.
    Herlocker, J. L., Konstan, J. A., Terveen, L.G., and Riedl, J. T., Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 1, 2004.CrossRefGoogle Scholar
  13. 13.
    Lam, S. K. and Riedl, J., Shilling recommender systems for fun and profit. In Proceedings of the 13th international Conference on World Wide Web, 2004.Google Scholar
  14. 14.
    Donath, J., and Boyd, D., Public displays of connection, BT Technology Journal 22(4):pp. 71–82, 2004.CrossRefGoogle Scholar
  15. 15.
    Resnick, P., and Zeckhauser, R., Trust Among Strangers in Internet Transactions: Empirical Analysis of eBay’s Reputation System. The Economics of the Internet and E-Commerce. Michael R. Baye, editor. Volume 11 of Advances in Applied Microeconomics. Amsterdam, pages 127–157, Elsevier Science, 2002.Google Scholar
  16. 16.
    P.O’Mahony, M., J.Hurley, N., Silvestre, N., Detecting Noise in Recommender System Databases, IUI’06, 2006.Google Scholar
  17. 17.
    Carter, J., Bitting, E., Ghorbani, A., Reputation Formalization for an Information-Sharing Multi-Agent System, Computational Intelligence 18(4), pages 515–534, 2002.CrossRefMathSciNetGoogle Scholar
  18. 18.
    Hill, W., Stead, L., Rosenstein, M., and Furnas, G. 1995. Recommending and evaluating choices in a virtual community of use. In Proceedings of the SIGCHI Conference on Human Factors in Computing System, 1995.Google Scholar
  19. 19.
    Josang, A., Modeling Trust in Information Security. PhD thesis, Norwegian University of Science and Technology, 1997.Google Scholar
  20. 20.
    Jøsang, A., Ismail, R., and Boyd, C., A Survey of Trust and Reputation Systems for Online Service Provision, Decision Support Systems, 2005.Google Scholar
  21. 21.
    Resnick, P., Iacovou, N., Suchak, M., Bergstrom, P., Riedl, J., GroupLens: An Open Architecture for Collaborative Filtering of Netnews, Proceedings of ACM 1994 Conference on Computer Supported Cooperative Work, 1994.Google Scholar

Copyright information

© International Federation for Information Processing 2007

Authors and Affiliations

  • Ebrahim Bagheri
    • 1
  • Ali A. Ghorbani
    • 1
  1. 1.Faculty of Computer ScienceUniversity of New BrunswickFrederictonCanada

Personalised recommendations