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Reseach Article

An Accuracy Improvement of Detection of Profile-Injection Attacks in Recommender Systems using Outlier Analysis

by Jiten H. Dhimmar, Raksha Chauhan
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 122 - Number 10
Year of Publication: 2015
Authors: Jiten H. Dhimmar, Raksha Chauhan
10.5120/21737-4930

Jiten H. Dhimmar, Raksha Chauhan . An Accuracy Improvement of Detection of Profile-Injection Attacks in Recommender Systems using Outlier Analysis. International Journal of Computer Applications. 122, 10 ( July 2015), 22-27. DOI=10.5120/21737-4930

@article{ 10.5120/21737-4930,
author = { Jiten H. Dhimmar, Raksha Chauhan },
title = { An Accuracy Improvement of Detection of Profile-Injection Attacks in Recommender Systems using Outlier Analysis },
journal = { International Journal of Computer Applications },
issue_date = { July 2015 },
volume = { 122 },
number = { 10 },
month = { July },
year = { 2015 },
issn = { 0975-8887 },
pages = { 22-27 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume122/number10/21737-4930/ },
doi = { 10.5120/21737-4930 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:10:13.185599+05:30
%A Jiten H. Dhimmar
%A Raksha Chauhan
%T An Accuracy Improvement of Detection of Profile-Injection Attacks in Recommender Systems using Outlier Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 122
%N 10
%P 22-27
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

E-Commerce recommender systems are affected by various kinds of profile-injection attacks where several fake user profiles are entered into the system to influence the recommendations made to the users. We have used Partition around Medoid (PAM) and Enhanced Clustering Large Applications Based on Randomized Search (ECLARANS) clustering algorithms of detecting such attacks by using outlier analysis. In user rating dataset, attack-profiles are considered as outliers in these algorithms. Firstly, we have used PAM and ECLARANS clustering algorithm in detecting the attack-profiles. These both algorithms have been applied for evaluating the performance of the system in identifying the attack profiles when they enter into the system. Experiments show that an accuracy of ECLARANS algorithm for detection of profile-injection attack for E-commerce recommender system is more than PAM clustering algorithm.

References
  1. Parthasarathi Chakraborty, "A Scalable Collaborative Filtering Based Recommender System using Incremental Clustering", IEEE International Advance Computing Conference, Patiala, India, 6-7 March 2009.
  2. Robin Burke, Bamshad Mobasher, Chad Williams and Runa Bhaumik, "Detecting Profile Injection Attacks in Collaborative Recommender Systems". In Proceedings of the 8th IEEE International Conference on E-Commerce Technology and the 3rd IEEE International Conference on Enterprise Computing, E-Commerce and E-Services, 2006.
  3. Kenneth Bryan, Michael O'Mahony and Padraig Cunningham, "Unsupervised Retrieval of Attack Profiles in Collaborative Recommender Systems", Technical Report UCD-CSI-2008-03, University College Dublin, April 2008.
  4. Parthasarathi Chakraborty and Sunil Karforma, "Detection of Profile-injection attacks in Recommender Systems using Outlier Analysis". In International Conference on Computational Intelligence: Modeling Techniques and Applications (CIMTA), Procedia Technology, Volume 10, 2013.
  5. Deepak Sinwar and Dr. Sudesh Kumar, "Study of Different Clustering Approaches for Outlier Detection", IJCSC, Volume 4, Number 2 September 2013.
  6. Ghazaleh Aghili, Mehdi Shajari, Shahram Khadivi and Mohammad Amin Morid, "Using Genre Interest of Users to Detect Profile Injection Attacks in Movie Recommender Systems". In Proceeding of IEEE International Conference on Machine Learning and Applications, 2011.
  7. Chad A. Williams, Bamshad Mobasher and Robin Burke, "Defending recommender systems: detection of profile injection attacks", SOCA, 2007.
  8. Hans-Peter Kriegel, Matthias Schubert and Arthur Zimek, "Angle-Based Outlier Detection in high-dimensional Data". In Proceeding of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pages 444–452, Las Vegas, NY, USA, 2008.
  9. Antonio Loureiro, Luis Torgo and Carlos Soares, "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.
  10. John Peter. S. , "An Efficient Algorithm for Local Outlier Detection Using Minimum Spanning Tree", International Journal of Research and Reviews in Computer Science, March 2011.
  11. Al-Zoubi and Moh'd Belal, "An Effective Clustering-Based Approach for Outlier Detection", European Journal of Scientific Research, Vol. 28, Issue 2, March 2009, pp. 310-316.
  12. S. Vijayarani and S. Nithya, "An Efficient Clustering Algorithm for Outlier Detection", International Journal of Computer Applications (0975 – 8887), Volume 32– No. 7, October 2011.
  13. "MovieLens dataset" available at: https://movielens. org/
Index Terms

Computer Science
Information Sciences

Keywords

PAM ECLARANS Outlier Analysis Recommender System profile-injection attack Attack-profile.