Effectiveness of Proximity-Based Outlier Analysis in Detecting Profile-Injection Attacks in E-Commerce Recommender Systems

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 340)

Abstract

E-Commerce recommender systems are vulnerable to different types of profile-injection attacks where a number of fake user profiles are inserted into the system to influence the recommendations made to the users. In this paper, we have used two proximity-based outlier detection strategies in identifying fake user profiles inserted into the recommender system by the attacker. The first strategy that has been used in detecting attack profiles is a k-Nearest Neighbor based algorithm. The second strategy used is a clustering-based algorithm in generating outlier score of each user profile in the system database. Three attack models namely random attack, average attack and bandwagon attack model have been considered for our analysis. Performance of the k-Nearest Neighbor-based and clustering-based outlier detection strategies have been analyzed for different attack percentages and different filler percentages of the attack profiles.

Keywords

Recommender system Outlier-detection Profile-injection attack Attack-profile K-nearest neighbor PAM CBLOF 

References

  1. 1.
    Lam, S., Riedl, J.: Shilling recommender systems for fun and profit. In: Proceedings of the 13th International WWW Conference, New York (2004)Google Scholar
  2. 2.
    Burke, R., Mobasher, B., Williams, C., Bhaumik, R.: Detecting profile injection attacks in collaborative recommender systems. In: Proceedings of the IEEE Joint Conference on Ecommerce Technology and Enterprise Computing, E-Commerce and E-Services. CEC/EEE 2006, Palo Alto, CA (2006)Google Scholar
  3. 3.
    Mehta, B.: Unsupervised shilling detection for collaborative filtering. Association for the Advancement of Artificial Intelligence (2007). www.aai.org
  4. 4.
    Loureiro, A., Torgo, L., Soares, C.: Outlier detection using clustering methods: a data cleaning application. In: Proceedings of KDNet Symposium on Knowledge-based Systems for the Public Sector. Bonn, Germany (2004)Google Scholar
  5. 5.
    John Peter, S.: An efficient algorithm for local outlier detection using minimum spanning tree. Int. J. Res. Rev. Comput. Sci. (IJRRCS). Department of computer science and research center St. Xavier’s College, Palayamkottai (2011)Google Scholar
  6. 6.
    Cutsem, B., Gath, I.: Detection of outliers and robust estimation using fuzzy clustering. Comput. Stat. Data Anal. 15, 47–61 (1993)CrossRefMATHGoogle Scholar
  7. 7.
    Acuna E., Rodriguez C.: A meta analysis study of outlier detection methods in classification. Technical paper, Department of Mathematics, University of Puerto Rico at Mayaguez, available at www.academic.uprm.edu/~eacuna/paperout.pdf. In: Proceedings IPSI 2004, Venice (2004)
  8. 8.
    Al-Zoubi, M.B.: An effective clustering-based approach for outlier detection. Eur. J. Sci. Res. 28(2), 310–316 (2009)Google Scholar
  9. 9.
    Eskin, E., Arnold, A., Prerau, M., Portnoy, L., Stolfo, S.: A geometric framework for unsupervised anomaly detection: detecting intrusions in unlabeled data. In: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 255–262. Morgan Kaufmann Publishers Inc, Los Altos (2000)Google Scholar
  10. 10.
    Portnoy, L., Eskin, E., Stolfo, S.: Intrusion detection with unlabeled data using clustering. In: Proceeding ACM Workshop on Data Mining Applied to Security (2001)Google Scholar
  11. 11.
    Bryan, M.O.K., Cunningham, P.: Unsupervised retrieval of attack profiles in collaborative recommender systems. In: Technical Report, University College Dublin (2008)Google Scholar
  12. 12.
    Kaufman, L., Rousseeuw, P.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)CrossRefMATHGoogle Scholar
  13. 13.
    Knorr, E., Ng, R.: Algorithms for mining distance-based outliers in large datasets. In: VLDB Conference (1998)Google Scholar
  14. 14.
    Ramaswamy, S., Rastogi, R., Shim, K.: Efficient algorithms for mining outliers from large data sets. In: ACM SIGMOD Conference, pp. 427–438 (2000)Google Scholar
  15. 15.
    He, Z., Xu, X., Deng, S.: Discovering cluster-based local outliers. Pattern Recogn. Lett. 24, 1641–1650 (2003)CrossRefMATHGoogle Scholar
  16. 16.
    MacQueen, J.: Some methods for classification and analysis of multivariate observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.University Institute of TechnologyUniversity of BurdwanBurdwanIndia
  2. 2.Department of Computer ScienceUniversity of BurdwanBurdwanIndia

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