KIISE Transactions on Computing Practices (정보과학회 컴퓨팅의 실제 논문지)

Korean Institute of Information Scientists and Engineers (한국정보과학회)
권호 표지
DOI QR코드

DOI QR Code

STA : Sybil Type-aware Robust Recommender System

시빌 유형을 고려한 견고한 추천시스템

  • Received : 2015.06.03
  • Accepted : 2015.08.20
  • Published : 2015.10.15
⟨ Previous Next ⟩

Abstract

With a rapid development of internet, many users these days refer to various recommender sites when buying items, movies, music and more. However, there are malicious users (Sybil) who raise or lower item ratings intentionally in these recommender sites. And as a result, a recommender system (RS) may recommend incomplete or inaccurate results to normal users. We suggest a recommender algorithm to separate ratings generated by users into normal ratings and outlier ratings, and to minimize the effects of malicious users. Specifically, our algorithm first ensures a stable RS against three kinds of attack models (Random attack, Average attack, and Bandwagon attack) which are the main recent security issues in RS. To prove the performance of the method of suggestion, we conducted performance analysis on real world data that we crawled. The performance analysis demonstrated that the suggested method performs well regardless of Sybil size and type when compared to existing algorithms.

최근 인터넷의 급 성장과 함께 사용자들은 물건이나 영화, 음악 등을 구매 할 때 여러 가지 추천 사이트를 활용한다. 하지만 이러한 추천 사이트에는 악의적으로 아이템의 평점을 높이거나 낮추려는 악의적인 사용자(Sybil)들이 존재할 수 있으며, 추천시스템에 영향을 끼쳐 일반 사용자들에게 부정확한 결과를 추천할 수 있다. 본 논문에서는 사용자들이 생성하는 평점들을 일반적인 평점과 일반적이지 않은 평점으로 구분하고, 상태 정보를 재정립 및 활용하여 악의적 사용자의 영향력을 최소화 하는 추천 알고리즘을 제안한다. 특히, 현재 추천시스템에서의 문제가 되고 있는 3가지 공격모델의 개별 특성을 고려하여 시빌 유형에 견고한 추천 시스템을 처음으로 제안한다. 제안하는 기법의 성능을 입증하기 위해 실제 데이터를 직접 수집(crawling)하여 성능분석결과 제안하는 기법의 성능이 기존 알고리즘과는 다르게 공격 크기 및 종류에 상관 없이 좋은 성능을 보이는 것을 확인 하였다.

Keywords

Acknowledgement

Supported by : 한국연구재단

References

  1. J. Douceur, "The sybil attack," Proc. of 1st International Workshop on Peer-to-Peer Systems, pp. 251-260, 2002.
  2. H. Yu, C. Shi, M. Kaminsky, P. Gibbsons and F. Xiao, "Dsybil: Optimal Sybil-Resistance for Recommendation Systems," Proc. of the 2009 30th IEEE Symposium on Security and Privacy, pp. 283-298, 2009.
  3. N. Hurley, "Robustness of Recommender Systems," Proc. of the fifth ACM conference on Recommender systems, pp. 9-10, 2011.
  4. Z. Cheng and N. Hurley, "Robust Collaborative Recommendation by Least Trimmed Squares Matrix Factorization," Proc. of the 2010 22nd IEEE International Conference on Tools with Artificial Intelligence, Vol. 02, pp. 105-112, 2010.
  5. Y. Koren, R. bell, and C. Volinsky, "Matrix factorization techniques for recommender systems," Vol. 42, No. 8, pp. 30-37, Aug. 2009. https://doi.org/10.1109/MC.2009.263
  6. M. P. O'Mahony, N. J. Hurley, and G. C. M. Silvestre, "Promoting recommendations: An attack on collaborative filtering," Proc. of the 13th International Conference on Database and Expert Systems Applications, pp. 494-503, 2002.
  7. R. Burke, B. Mobasher, and R. Bhaumik, "Limited knowledge shilling attacks in collaborative filtering systems," Proc. of the 3rd IJCAI Workshop in Intelligent Techniques for Personalization, 2005.
  8. B. Mobasher, R. Burke, and J. Sandvig, "Modelbased collaborative filtering as a defense against profile injection attacks," Proc. of the 21st National Conference on Artificial Intelligence and the 18th Innovative Applications of rtificial Intelligence Conference, Vol. 2, pp. 1388-1393, 2006.
  9. B. Mehta, "Unsupervised shilling detection for collaborative filtering," Proc. of the 22nd national conference on Artificial intelligence, pp. 1402-1407, 2007.
  10. Wei Zhou, Junhao Wen, Yun Sing Koh, Shafiq Alam and Gillian Dobbie, "Attack detection in recommender systems based on target item analysis," Proc. of the Neural Networks (IJCNN) and International Joint Conference, pp. 332-339, 2014.
  11. Zhuo Zhang, Sanjeev R. Kulkarni, "Detection of Shilling Attacks in Recommender Systems via Spectral Clustering," Proc. Information Fusion (FUSION) and 17th International Conference, pp. 1-8, 2014.
  12. P.-A. Chirita, W. Nejdl, and C. Zamfir, "Preventing shilling attacks in online recommender systems," Proc. 7th Annual ACM International Workshop on Web Information and Data Management, pp. 67-74, 2005.
  13. LAM, S. AND RIEDL, J., "Shilling recommender systems for fun and profit," Proc. of the 13th International WWW Conference, pp. 393-402, 2004.
  14. [Online]. Available: http://news.naver.com/main/read.nhn?mode=LSD&mid=sec&sid1=106&oid=038&aid=0002369632

Cited by

  1. Bipartite Preference aware Robust Recommendation System vol.26, pp.4, 2016, https://doi.org/10.13089/JKIISC.2016.26.4.953
  2. Relationship Analysis between Malware and Sybil for Android Apps Recommender System vol.26, pp.5, 2016, https://doi.org/10.13089/JKIISC.2016.26.5.1235