CFP last date
20 March 2024
Reseach Article

Clustering Approach to detect Profile Injection Attacks in Recommender System

by Ashish Kumar, Deepak Garg, Prashant Singh Rana
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 166 - Number 6
Year of Publication: 2017
Authors: Ashish Kumar, Deepak Garg, Prashant Singh Rana
10.5120/ijca2017914031

Ashish Kumar, Deepak Garg, Prashant Singh Rana . Clustering Approach to detect Profile Injection Attacks in Recommender System. International Journal of Computer Applications. 166, 6 ( May 2017), 7-11. DOI=10.5120/ijca2017914031

@article{ 10.5120/ijca2017914031,
author = { Ashish Kumar, Deepak Garg, Prashant Singh Rana },
title = { Clustering Approach to detect Profile Injection Attacks in Recommender System },
journal = { International Journal of Computer Applications },
issue_date = { May 2017 },
volume = { 166 },
number = { 6 },
month = { May },
year = { 2017 },
issn = { 0975-8887 },
pages = { 7-11 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume166/number6/27672-2017914031/ },
doi = { 10.5120/ijca2017914031 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:12:58.319976+05:30
%A Ashish Kumar
%A Deepak Garg
%A Prashant Singh Rana
%T Clustering Approach to detect Profile Injection Attacks in Recommender System
%J International Journal of Computer Applications
%@ 0975-8887
%V 166
%N 6
%P 7-11
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recommender systems apply techniques of knowledge discovery for specific problem to make personalized recommendation of the products or services to the users. The huge growth in the information and the count of visitors to the web sites especially on e-commerce in last few years creates some challenges for recommender systems. E-commerce recommender systems are vulnerable to the profile injection attacks, involving insertion of fake profiles into the system to influence the recommendations made to the users. Prior work has shown that performance of system can be affected by even small number of biased profiles. In this paper, we show that unsupervised clustering approach can be used effectively for the detection of profile injection attacks in recommender system. Here we give a comparative study of four clustering algorithms and measure their performance.

References
  1. Davoodi, Fatemeh Ghiyafeh, and Omid Fatemi. "Tag based recommender system for social bookmarking sites." In Proceedings of the 2012 International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2012), pp. 934-940. IEEE Computer Society, 2012.
  2. Bobadilla, Jesús, Fernando Ortega, Antonio Hernando, and Abraham Gutiérrez. "Recommender systems survey." Knowledge-Based Systems 46 (2013): 109-132.
  3. Burke, Robin, Bamshad Mobasher, Roman Zabicki, and Runa Bhaumik. "Identifying attack models for secure recommendation." In Beyond Personalization: A Workshop on the Next Generation of Recommender Systems. 2005.
  4. Linden, Greg, Brent Smith, and Jeremy York. "Amazon.com recommendations: Item-to-item collaborative filtering." Internet Computing, IEEE 7, no. 1 (2003): 76-80.
  5. O'Mahony, Michael, Neil Hurley, Nicholas Kushmerick, and Guénolé Silvestre. "Collaborative recommendation: A robustness analysis." ACM Transactions on Internet Technology (TOIT) 4, no. 4 (2004): 344-377.
  6. Lam, Shyong K., and John Riedl. "Shilling recommender systems for fun and profit." In Proceedings of the 13th international conference on World Wide Web, pp. 393-402. ACM, 2004.
  7. Burke, Robin, Bamshad Mobasher, Runa Bhaumik, and Chad Williams. "Segment-based injection attacks against collaborative filtering recommender systems." In Data Mining, Fifth IEEE International Conference on, pp. 4-pp. IEEE, 2005.
  8. Herlocker, Jonathan L., Joseph A. Konstan, Loren G. Terveen, and John T. Riedl. "Evaluating collaborative filtering recommender systems." ACM Transactions on Information Systems (TOIS) 22, no. 1 (2004): 5-53.
  9. Burke, Robin, Bamshad Mobasher, Chad Williams, and Runa Bhaumik. "Classification features for attack detection in collaborative recommender systems." In Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 542-547. ACM, 2006.
  10. O'Mahony, Michael P., Neil J. Hurley, and Guénolé Silvestre. "Detecting noise in recommender system databases." In Proceedings of the 11th international conference on Intelligent user interfaces, pp. 109-115. ACM, 2006.
  11. Williams, Chad A., Bamshad Mobasher, and Robin Burke. "Defending recommender systems: detection of profile injection attacks." Service Oriented Computing and Applications 1, no. 3 (2007): 157-170.
  12. Fu, Lin, Dion Hoe-Lian Goh, Schubert Shou-Boon Foo, and Jin-Cheon Na. Collaborative querying through a hybrid query clustering approach. Springer Berlin Heidelberg, 2003.
  13. Lee, C-H., Y-H. Kim, and P-K. Rhee. "Web personalization expert with combining collaborative filtering and association rule mining technique." Expert Systems with Applications 21, no. 3 (2001): 131-137.
  14. Zhang, Sheng, Yi Ouyang, James Ford, and Fillia Makedon. "Analysis of a low-dimensional linear model under recommendation attacks." In Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval, pp. 517-524. ACM, 2006.
  15. Mehta, Bhaskar, and Wolfgang Nejdl. "Unsupervised strategies for shilling detection and robust collaborative filtering." User Modeling and User-Adapted Interaction 19, no. 1-2 (2009): 65-97.
  16. Hurley, Neil, Zunping Cheng, and Mi Zhang. "Statistical attack detection." In Proceedings of the third ACM conference on Recommender systems, pp. 149-156. ACM, 2009.
  17. Fu, Lin, Dion Hoe-Lian Goh, Schubert Shou-Boon Foo, and Jin-Cheon Na. Collaborative querying through a hybrid query clustering approach. Springer Berlin Heidelberg, 2003.
  18. Shardanand, Upendra, and Pattie Maes. "Social information filtering: algorithms for automating “word of mouth”." In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 210-217. ACM Press/Addison-Wesley Publishing Co., 1995.
  19. Hill, Will, Larry Stead, Mark Rosenstein, and George Furnas. "Recommending and evaluating choices in a virtual community of use." In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 194-201. ACM Press/Addison-Wesley Publishing Co., 1995.
  20. Konstan, Joseph A., Bradley N. Miller, David Maltz, Jonathan L. Herlocker, Lee R. Gordon, and John Riedl. "GroupLens: applying collaborative filtering to Usenet news." Communications of the ACM 40, no. 3 (1997): 77-87.
  21. Resnick, Paul, Neophytos Iacovou, Mitesh Suchak, Peter Bergstrom, and John Riedl. "GroupLens: an open architecture for collaborative filtering of netnews." In Proceedings of the 1994 ACM conference on Computer supported cooperative work, pp. 175-186. ACM, 1994.
  22. Su, Xiaoyuan, and Taghi M. Khoshgoftaar. "A survey of collaborative filtering techniques." Advances in artificial intelligence 2009 (2009): 4.
  23. Chirita, Paul-Alexandru, Wolfgang Nejdl, and Cristian Zamfir. "Preventing shilling attacks in online.
  24. Kumar, Ashish, Deepak Garg, and Prashant Singh Rana. "Ensemble approach to detect profile injection attack in recommender system." Advances in Computing, Communications and Informatics (ICACCI), 2015 International Conference on. IEEE, 2015.
  25. Noh, Giseop, Young-myoung Kang, Hayoung Oh, and Chong-kwon Kim. "Robust Sybil attack defense with information level in online Recommender Systems." Expert Systems with Applications 41, no. 4 (2014): 1781-1791.
Index Terms

Computer Science
Information Sciences

Keywords

Recommender system collaborative filtering attack detection bias profile injection performance measure unsupervised approach.