Shilling Attacks Detection in Collaborative Recommender System: Challenges and Promise

The reliability of the recommender system is highly essential for the continuity of any system. Fake and malicious users may be spoiling system predictions reliability by inserting and injecting fake profiles called “shilling attacks” into the target recommender system. Thus, the detection of these attacks is necessary for any recommender system. Therefore, several shilling attacks detection approaches have proposed. In this work, we propose a survey for the recent detection methods, which pick up famous shilling attack models against the collaborative filtering recommender systems.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Subscribe and save

Springer+ Basic €32.70 /Month

Buy Now

Price includes VAT (France)

eBook EUR 234.33 Price includes VAT (France)

Softcover Book EUR 295.39 Price includes VAT (France)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Similar content being viewed by others

Shilling attacks against collaborative recommender systems: a review

Article 19 September 2018

A State of Art Survey on Shilling Attack in Collaborative Filtering Based Recommendation System

Chapter © 2016

Analysis on Detection of Shilling Attack in Recommendation System

Chapter © 2021

References

  1. Jiang, L., Cheng, Y., Yang, L., Li, J., Yan, H., Wang, X.: A trust-based collaborative filtering algorithm for e-commerce recommendation system. J. Ambient. Intel. Hum. Comput. 10, 1–12 (2018) Google Scholar
  2. Jiang, L., Cheng, Y., Yang, L., et al.: A trust-based collaborative filtering algorithm for E-commerce recommendation system. J. Ambient Intell. Hum. Comput. 10, 3023–3034 (2019) ArticleGoogle Scholar
  3. Gunes, I., Kaleli, C., Bilge, A., Polat, H.: Shilling attacks against recommender systems: a comprehensive survey. Artif. Intell. Rev. 42(4), 767–799 (2014) ArticleGoogle Scholar
  4. Dou, T., Yu, J., Xiong, Q., Gao, M., Song, Y., Fang, Q.: Collaborative shilling detection bridging factorization and user embedding. In: Proceedings of the 13th EAI International Conference on Collaborative Computing: Networking, Applications and Worksharing. Guangzhou, China (2017) Google Scholar
  5. Hao, Y., Zhang, P., Zhang, F.: Multiview ensemble method for detecting shilling attacks in collaborative recommender systems. Secur. Commun. Netw. 2018, 1–33 (2018). https://doi.org/10.1155/2018/8174603. Article ID 8174603 ArticleGoogle Scholar
  6. Hao, Y., Zhang, F., Wang, J., Zhao, Q., Cao, J.: Detecting shilling attacks with automatic features from multiple views. Secur. Commun. Netw. 2019, 1–13 (2019). https://doi.org/10.1155/2019/6523183. Article ID 6523183 ArticleGoogle Scholar
  7. Si, M., Li, Q.: Shilling attacks against collaborative recommender systems: a review. Artif. Intell. Rev. 53, 291–319 (2020). https://doi.org/10.1007/s10462-018-9655-xArticleGoogle Scholar
  8. Bhaumik, R., et al.: Securing collaborative filtering against malicious attacks through anomaly detection. In: Proceedings of the 4th Workshop on Intelligent Techniques for Web Personalization (ITWP’06), vol. 6. Boston (2006) Google Scholar
  9. Mobasher, B.: Toward trustworthy recommender systems: an analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7(4), 23-es (2007) Google Scholar
  10. Alonso, S., et al.: Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems. IEEE Access 7, 41782–41798 (2019) ArticleGoogle Scholar
  11. Burke, R., et al.: Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM (2006) Google Scholar
  12. Mobasher, B., Burke, R., Bhaumik, R., Sandvig, J.J.: Attacks and remedies in collaborative recommendation. IEEE Intell. Syst. 22(3), 56–63 (2007) ArticleGoogle Scholar
  13. Chen, K., et al.: Shilling attack based on item popularity and rated item correlation against collaborative filtering. Int. J. Mach. Learn. Cybern. 10(7), 1833–1845 (2019) ArticleGoogle Scholar
  14. Batmaz, Z., Yilmazel, B., Kaleli, C.: Shilling attack detection in binary data: a classification approach. J. Ambient Intell. Hum. Comput. (2019). https://doi.org/10.1007/s12652-019-01321-2
  15. Gao, M., et al.: Detection of abnormal item based on time intervals for recommender systems. Sci. World J. 2014, 1–8 (2014). https://doi.org/10.1155/2014/845897. Article ID 845897 Google Scholar
  16. Xia, H., Fang, B., Gao, M., Ma, H., Tang, Y., Wen, J.: A novel item anomaly detection approach against shilling attacks in collaborative recommendation systems using the dynamic time interval segmentation technique. Inf. Sci. 306, 150–165 (2015) ArticleGoogle Scholar
  17. Batmaz, Z.: Shilling attack design and detection on masked binary data. MS thesis, Anadolu Üniversitesi (2015) Google Scholar
  18. Liu, X., et al.: a novel kalman filter based shilling attack detection algorithm. arXiv preprint arXiv:1908.06968 (2019)
  19. Karthikeyan, P., Selvi, S.T., Neeraja, G., Deepika, R., Vincent, A., Abinaya, V.: Prevention of shilling attack in recommender systems using discrete wavelet transform and support vector machine. In: Proceedings of the 8th International Conference on Advanced Computing (2016) Google Scholar
  20. Shen, H., et al.: Discovering social spammers from multiple views. Neurocomputing 225, 49–57 (2017) ArticleGoogle Scholar
  21. Zhou, W., et al.: Shilling attack detection for recommender systems based on credibility of group users and rating time series. PLoS ONE 13(5), e0196533 (2018) ArticleGoogle Scholar
  22. Forestier, G., Wemmert, C.: Semi-supervised learning using multiple clusterings with limited labeled data. Inf. Sci. 361, 48–65 (2016) ArticleGoogle Scholar
  23. O’Mahony, M., et al.: Collaborative recommendation: A robustness analysis. ACM Trans. Internet Technol. 4(4), 344–377 (2004) ArticleGoogle Scholar
  24. Kapoor, S., Kapoor, V., Kumar, R.: A review of attacks and its detection attributes on collaborative recommender systems. Int. J. Adv. Res. Comput. Sci. 8(7), 1188–1193 (2017) ArticleGoogle Scholar
  25. Mehta, B., Nejdl, W.: Unsupervised strategies for shilling detection and robust collaborative filtering. User Model. User-Adapt. Interact. 19(1–2), 65–97 (2009) ArticleGoogle Scholar
  26. Liu, M., Xu, C., et al.: Fast SVM trained by divide-and-conquer anchors. In: IJCAI’17 Proceedings of the 26th International Joint Conference on Artificial Intelligence, pp. 2322–2328 (2017) Google Scholar
  27. Zhou, W., et al.: Shilling attack detection for recommender systems based on credibility of group users and rating time series. PLoS ONE 13(5) (2018) Google Scholar
  28. Zhou, W., et al.: SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems. Neurocomputing 210, 197–205 (2016) ArticleGoogle Scholar

Author information

Authors and Affiliations

  1. Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, Egypt Reda A. Zayed, Lamiaa F. Ibrahim & Hesham A. Hefny
  2. Management Information System Department, Higher Institute of Computer and Information Technology, Al Shorouk, Egypt Hesham A. Salman
  1. Reda A. Zayed