1. Resnick P., Iacovou N., Suchak M., Bergstrom P. and Riedl, J. Grouplens: An open architecture for collaborative filtering of netnews. In Proceedings of the1994 ACM conference on Computer supported cooperative work (pp. 175-186), 1994, October. 2. Davoodi, F.G. and Fatemi, O. Tag based recommender system for social bookmarking sites. In2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (pp. 934-940). IEEE, 2012, August. 3. Bobadilla J., Ortega F., Hernando A. and Gutiérrez A.Recommender systems survey. Knowledge-based systems, vol. 46, pp.109-132, 2013 4. Lam, S.K. and Riedl, J. Shilling recommender systems for fun and profit. In Proceedings of the 13th international conference on World Wide Web (pp. 393-402), 2004, May. 5. Bland J.A., Petty M.D., Whitaker T.S., Maxwell K.P. and Cantrell W.A.Machine learning cyberattack and defense strategies. Computers & security, vol. 92, pp. 101738, 2020 6. Chen K., Chan P.P., Zhang F. and Li Q.Shilling attack based on item popularity and rated item correlation against collaborative filtering. International Journal of Machine Learning and Cybernetics, vol. 10, pp.1833-1845, 2019 7. Si, M. and Li, Q.Shilling attacks against collaborative recommender systems: a review. Artificial Intelligence Review, vol. 53, pp.291-319, 2020 8. Aashkaar, M. and Sharma, P. Enhanced energy efficient AODV routing protocol for MANET. In2016 International Conference on Research Advances in Integrated Navigation Systems (RAINS) (pp. 1-5). IEEE, 2016, May. 9. Kaur, P. and Goel, S. Shilling attack models in recommender system. In2016 International conference on inventive computation technologies (ICICT) (Vol. 2, pp. 1-5). IEEE, 2016, August. 10. Bilge, A., Ozdemir, Z. and Polat, H.A novel shilling attack detection method. Procedia Computer Science, 31, pp.165-174, 2014 11. Batmaz, Z., Yilmazel, B. and Kaleli, C.Shilling attack detection in binary data: a classification approach. Journal of Ambient Intelligence and Humanized Computing, vol. 11, pp.2601-2611, 2020 12. Zhang, F. and Zhou, Q.Ensemble detection model for profile injection attacks in collaborative recommender systems based on BP neural network. IET Information Security, vol. 9, no. 1, pp.24-31, 2015 13. Wang Y., Qian L., Li F. and Zhang L.A comparative study on shilling detection methods for trustworthy recommendations. Journal of Systems Science and Systems Engineering, vol. 27, no. 4, pp.458-478, 2018 14. Sundar A.P., Li F., Zou X., Gao T. and Russomanno E.D.Understanding Shilling Attacks and Their Detection Traits: A Comprehensive Survey. IEEE Access, vol. 8, pp.171703-171715, 2020 15. Hu D., Xu B., Wang J., Han L. and Liu J. A Shilling Attack Model Based on TextCNN. In2020 IEEE 3rd International Conference on Automation, Electronics and Electrical Engineering (AUTEEE) (pp. 282-289). IEEE, 2020, November. 16. Ebrahimian, M. and Kashef, R. Efficient Detection of Shilling's Attacks in Collaborative Filtering Recommendation Systems Using Deep Learning Models. In2020 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM) (pp. 460-464). IEEE, 2020, December. 17. Rani S., Kaur M., Kumar M., Ravi V., Ghosh U. and Mohanty J.R.Detection of shilling attack in recommender system for YouTube video statistics using machine learning techniques. Soft Computing, pp.1-13, 2021 18. Sharma P., Saxena K., andSharma R.Heart disease prediction system evaluation using C4.5 rules and partial tree doi:10.1007/978-81-322-2731-1_26, 2016 19. Williams, C.A., Mobasher, B. and Burke, R.Defending recommender systems: detection of profile injection attacks. Service Oriented Computing and Applications, vol. 1, no. 3, pp.157-170, 2007 20. Bilge, A., Ozdemir, Z. and Polat, H.A novel shilling attack detection method. Procedia Computer Science, vol. 31, pp.165-174, 2014 21. Alonso S., Bobadilla J., Ortega F. and Moya R.Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems. IEEE Access, vol. 7, pp.41782-41798, 2019 22. Zhang, F. and Zhou, Q. Ensemble detection model for profile injection attacks in collaborative, 2014 23. Williams, C.A., Mobasher, B. and Burke, R.Defending recommender systems: detection of profile injection attacks. Service Oriented Computing and Applications, vol. 1, no. 3, pp.157-170, 2007 24. Yang Z., Xu L., Cai Z. and Xu Z.Re-scale AdaBoost for attack detection in collaborative filtering recommender systems. Knowledge-Based Systems, vol. 100, pp.74-88, 2016 25. Mehta, B., Hofmann, T. and Fankhauser, P. Lies and propaganda: detecting spam users in collaborative filtering. In Proceedings of the 12th international conference on Intelligent user interfaces (pp. 14-21), 2007, January. 26. Zhou, Q., Wu, J. and Duan, L.Recommendation attack detection based on deep learning. Journal of Information Security and Applications, vol. 52, pp. 102493, 2020 27. Zhou W., Wen J., Xiong Q., Gao M. and Zeng J.SVM-TIA a shilling attack detection method based on SVM and target item analysis in recommender systems. Neurocomputing, vol. 210, pp.197-205, 2016 |