Detecting Group Shilling Attacks in Online Recommender Systems Based on Bisecting K-Means Clustering

被引:25
作者
Zhang, Fuzhi [1 ,2 ]
Wang, Shilei [1 ,2 ]
机构
[1] Yanshan Univ, Sch Informat Sci & Engn, Qinhuangdao 066000, Hebei, Peoples R China
[2] Key Lab Comp Virtual Technol & Syst Integrat Hebe, Qinhuangdao 066000, Hebei, Peoples R China
基金
中国国家自然科学基金;
关键词
Recommender systems; Feature extraction; Clustering algorithms; Principal component analysis; Target tracking; Support vector machines; Bisecting K-means clustering; group shilling attack detection; group shilling attacks; recommender systems;
D O I
10.1109/TCSS.2020.3013878
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Existing shilling attack detection approaches focus mainly on identifying individual attackers in online recommender systems and rarely address the detection of group shilling attacks in which a group of attackers colludes to bias the output of an online recommender system by injecting fake profiles. In this article, we propose a group shilling attack detection method based on the bisecting K-means clustering algorithm. First, we extract the rating track of each item and divide the rating tracks to generate candidate groups according to a fixed time interval. Second, we propose item attention degree and user activity to calculate the suspicious degrees of candidate groups. Finally, we employ the bisecting K-means algorithm to cluster the candidate groups according to their suspicious degrees and obtain the attack groups. The results of experiments on the Netflix and Amazon data sets indicate that the proposed method outperforms the baseline methods.
引用
收藏
页码:1189 / 1199
页数:11
相关论文
共 28 条
[1]  
[Anonymous], 2012, CHINESE J COMPUTERS
[2]  
Bryan K, 2008, RECSYS'08: PROCEEDINGS OF THE 2008 ACM CONFERENCE ON RECOMMENDER SYSTEMS, P155
[3]  
Burke R., 2006, KDD, P542
[4]   Detecting opinion spammer groups and spam targets through community discovery and sentiment analysis [J].
Choo E. ;
Yu T. ;
Chi M. .
Journal of Computer Security, 2017, 25 (03) :283-318
[5]   Item Anomaly Detection Based on Dynamic Partition for Time Series in Recommender Systems [J].
Gao, Min ;
Tian, Renli ;
Wen, Junhao ;
Xiong, Qingyu ;
Ling, Bin ;
Yang, Linda .
PLOS ONE, 2015, 10 (08)
[6]   Shilling attacks against recommender systems: a comprehensive survey [J].
Gunes, Ihsan ;
Kaleli, Cihan ;
Bilge, Alper ;
Polat, Huseyin .
ARTIFICIAL INTELLIGENCE REVIEW, 2014, 42 (04) :767-799
[7]  
[韩忠明 Han Zhongming], 2017, [计算机学报, Chinese Journal of Computers], V40, P939
[8]   Collaborative Filtering with Temporal Dynamics [J].
Koren, Yehuda .
COMMUNICATIONS OF THE ACM, 2010, 53 (04) :89-97
[9]  
Lam S.K., 2004, P 13 INT C WORLD WID, P393, DOI DOI 10.1145/988672.988726
[10]  
[李文涛 Li Wentao], 2015, [自动化学报, Acta Automatica Sinica], V41, P1563