Fusing hypergraph spectral features for shilling attack detection

Journal of Information Security and Applications - Tập 63 - Trang 103051 - 2021
Hao Li1,2,3, Min Gao1,2, Fengtao Zhou2, Yueyang Wang2, Qilin Fan2, Linda Yang4
1Key Laboratory of Dependable Service Computing in Cyber Physical Society (Chongqing University), Ministry of Education, Chongqing, 400044, China
2School of Big Data & Software Engineering, Chongqing University, Chongqing 401331, China
3School of Automotive Engineering, Chongqing University, Chongqing 400044, China
4School of Engineering, University of Portsmouth, Portsmouth, PO1 3AH, UK

Tài liệu tham khảo

Gao, 2010, Personalisation in web computing and informatics: Theories, techniques, applications, and future research, Inf Syst Front, 12, 607, 10.1007/s10796-009-9199-3 Gao, 2011, Userrank for item-based collaborative filtering recommendation, Inform Process Lett, 111, 440, 10.1016/j.ipl.2011.02.003 Yu, 2017, Hybrid attacks on model-based social recommender systems, Physica A, 483, 171, 10.1016/j.physa.2017.04.048 Anelli, 2020, Sasha: Semantic-aware shilling attacks on recommender systems exploiting knowledge graphs, vol. 12123, 307 Alonso, 2019, Robust model-based reliability approach to tackle shilling attacks in collaborative filtering recommender systems, IEEE Access, 7, 41782, 10.1109/ACCESS.2019.2905862 Deldjoo, 2019, Assessing the impact of a user-item collaborative attack on class of users, vol. 2462 Zheng, 2018, A novel social network hybrid recommender system based on hypergraph topologic structure, World Wide Web, 21, 985, 10.1007/s11280-017-0494-5 Deldjoo, 2021, A survey on adversarial recommender systems: From attack/defense strategies to generative adversarial networks, ACM Comput Surv, 54, 35:1 Gunes, 2014, Shilling attacks against recommender systems: a comprehensive survey, Artif Intell Rev, 42, 767, 10.1007/s10462-012-9364-9 Burke R, Mobasher B, Williams C, Bhaumik R. Classification features for attack detection in collaborative recommender systems. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2006, p. 542–7. Li, 2016, Shilling attack detection in recommender systems via selecting patterns analysis, IEICE Trans Inf Syst, 99, 2600, 10.1587/transinf.2015EDP7500 Song, 2017, Pud: Social spammer detection based on pu learning, 177 Dou, 2017, Collaborative shilling detection bridging factorization and user embedding, 459 Yang, 2018, Detection of shilling attack based on bayesian model and user embedding, 639 Yu W, Qin Z. Spectrum-enhanced pairwise learning to rank. In: The world wide web conference. 2019, p. 2247–57. Chirita P-A, Nejdl W, Zamfir C. Preventing shilling attacks in online recommender systems. In: Proceedings of the 7th annual ACM international workshop on web information and data management. 2005, p. 67–74. Cai, 2019, Detecting shilling attacks in recommender systems based on analysis of user rating behavior, Knowl-Based Syst, 177, 22, 10.1016/j.knosys.2019.04.001 Zhang S, Yin H, Chen T, Hung QVN, Huang Z, Cui L. GCN-based user representation learning for unifying robust recommendation and fraudster detection. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval. 2020, p. 689–98. Guo, 2018, Learning sequential behavior representations for fraud detection, 127 White, 2015 Zhang, 2014, Detection of shilling attacks in recommender systems via spectral clustering, 1 Gunes, 2014, Shilling attacks against recommender systems: a comprehensive survey, Artif Intell Rev, 42, 767, 10.1007/s10462-012-9364-9 Hurley N, Cheng Z, Zhang M. Statistical attack detection. In: Proceedings of the third ACM conference on recommender systems. 2009, p. 149–56. Williams, 2006, Detecting profile injection attacks in collaborative filtering: a classification-based approach, 167 Mobasher, 2007, Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness, ACM Trans Internet Technol, 7, 23, 10.1145/1278366.1278372 Batmaz, 2020, Shilling attack detection in binary data: a classification approach, J Ambient Intell Humaniz Comput, 11, 2601, 10.1007/s12652-019-01321-2 Zhou, 2020, Recommendation attack detection based on deep learning, J Inf Secur Appl, 52 Wu Z, Wu J, Cao J, Tao D. HySAD: A semi-supervised hybrid shilling attack detector for trustworthy product recommendation. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. 2012, p. 985–93. Cao, 2013, Shilling attack detection utilizing semi-supervised learning method for collaborative recommender system, World Wide Web, 16, 729, 10.1007/s11280-012-0164-6 Wu, 2015, Spammers detection from product reviews: a hybrid model, 1039 Zheng P, Yuan S, Wu X, Li J, Lu A. One-class adversarial nets for fraud detection. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33. 2019, p. 1286–93. Cai, 2019, An unsupervised method for detecting shilling attacks in recommender systems by mining item relationship and identifying target items, Comput J, 62, 579, 10.1093/comjnl/bxy124 Zhang, 2013, Graph-based detection of shilling attacks in recommender systems, 1 Zhang, 2020, Graph embedding-based approach for detecting group shilling attacks in collaborative recommender systems, Knowl Based Syst, 199, 10.1016/j.knosys.2020.105984 Yang, 2021, Graphlshc: Towards large scale spectral hypergraph clustering, Inform Sci, 544, 117, 10.1016/j.ins.2020.07.018 Zhou, 2007, Learning with hypergraphs: Clustering, classification, and embedding, 1601 Van Lierde H, Chow TW. A hypergraph model for incorporating social interactions in collaborative filtering. In: Proceedings of the 2017 international conference on data mining, communications and information technology. 2017, p. 1–6. Zhang, 2014, HHT–SVM: An online method for detecting profile injection attacks in collaborative recommender systems, Knowl-Based Syst, 65, 96, 10.1016/j.knosys.2014.04.020 Xu C, Zhang J, Chang K, Long C. Uncovering collusive spammers in Chinese review websites. In: Proceedings of the 22nd ACM international conference on information & knowledge management. 2013, p. 979–88. Lin C, Chen S, Li H, Xiao Y, Li L, Yang Q. Attacking recommender systems with augmented user profiles. In: Proceedings of the 29th ACM international conference on information & knowledge management. 2020, p. 855–64. Zhang Y, Tan Y, Zhang M, Liu Y, Chua T-S, Ma S. Catch the black sheep: unified framework for shilling attack detection based on fraudulent action propagation. In: Twenty-fourth international joint conference on artificial intelligence. 2015. Deldjoo, 2020, How dataset characteristics affect the robustness of collaborative recommendation models, 951 Maaten, 2008, Visualizing data using t-SNE, J Mach Learn Res, 9, 2579