Detection of Trust Shilling Attacks in Recommender Systems

被引：0
作者：
Chen X. [1 ]
Deng X. [2 ]
Huang C. [2 ]
Shin H. [1 ]
机构：
[1] Dept. of Computer Engineering, Konkuk University, Seoul
来源：
IEICE Transactions on Information and Systems | 2022年 / E105D卷 / 06期
基金：
新加坡国家研究基金会;
关键词：
recommender systems; shilling attacks detection; trust shilling attacks;
D O I：
10.1587/TRANSINF.2021EDL8094
中图分类号：
学科分类号：
摘要：
Most research on detecting shilling attacks focuses on users’ rating behavior but does not consider that attackers may also attack the users’ trusting behavior. For example, attackers may give a low score to other users’ ratings so that people would think the ratings from the users are not helpful. In this paper, we define the trust shilling attack, propose the behavior features of trust attacks, and present an effective detection method using machine learning methods. The experimental results demonstrate that, based on our proposed behavior features of trust attacks, we can detect trust shilling attacks as well as traditional shilling attacks accurately. Copyright © 2022 The Institute of Electronics, Information and Communication Engineers
引用
收藏
页码：1239 / 1242
页数：3
相关论文
共 18 条
  • [1] Burke R., Mobasher B., Williams C., Bhaumik R., Classification Features for Attack Detection in Collaborative Recommender Systems, Proc. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 542-547, (2006)
  • [2] Yang Z., Cai Z., Guan X., Estimating User Behavior toward Detecting Anomalous Ratings in Rating Systems, Knowledge-Based Systems, 111, pp. 144-158, (2016)
  • [3] Yang Z., Cai Z., Yang Y., Spotting Anomalous Ratings for Rating Systems by Analyzing Target Users and Items, Neurocomputing, 240, pp. 25-46, (2017)
  • [4] Zhang F., Zhang Z., Zhang P., Wang S., UD-HMM: An Unsupervised Method for Shilling Attack Detection based on Hidden Markov Model and Hierarchical Clustering, Knowledge-Based Systems, 148, pp. 146-166, (2018)
  • [5] Williams C.A., Mobasher B., Burke R., Bhaumik R., Detecting Profile Injection Attacks in Collaborative Filtering: a Classification-based Approach, Proc. Knowledge Discovery on the Web International Conference on Advances in Web Mining and Web Usage Analysis, pp. 167-186, (2006)
  • [6] Yang Z., Xu L., Cai Z., Xu Z., Re-scale Adaboost for Attack Detection in Collaborative Filtering Recommender Systems, Knowledge-Based System, 100, pp. 74-88, (2016)
  • [7] Zhou W., Wen J., Xiong Q., Gao M., Zeng J., SVM-TIA A Shilling Attack Detection Method based on SVM and Target Item Analysis in Recommender Systems, Neurocomputing, 210, pp. 197-205, (2016)
  • [8] Zhang F., Chen H., An Ensemble Method for Detecting Shilling Attacks Based on Ordered Item Sequences, Security and Communication Networks, 9, 7, pp. 680-696, (2016)
  • [9] Zhou Q., Supervised Approach for Detecting Average Over Popular Items Attack in Collaborative Recommender Systems, IET Information Security, 10, 3, pp. 134-141, (2016)
  • [10] Cai H., Zhang F., Detecting shilling attacks in recommender systems based on analysis of user rating behavior, Knowledge-Based Systems, 177, pp. 22-43, (2019)
12