期刊
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
卷 33, 期 4, 页码 1375-1388出版社
IEEE COMPUTER SOC
DOI: 10.1109/TKDE.2019.2946247
关键词
Feature extraction; Recommender systems; Collaboration; Training; Correlation; Clustering algorithms; Detection algorithms; Collaborative recommender systems; shilling attacks detection; behavior analysis; spectral clustering
类别
资金
- National Natural Science Foundation of China [61772452, 61379116]
- Natural Science Foundation of Hebei Province, China [F2015203046, F2014201165]
- Key Program of Research on Science and Technology of Higher Education Institutions of Hebei Province, China [ZD2016043]
The paper presents an unsupervised approach BS-SC for detecting shilling profiles, which does not require knowledge of attack size or labeling of candidate spammers. By analyzing user behaviors and utilizing behavior features extraction and behavior similarity matrix clustering, BS-SC effectively distinguishes between shilling profiles and genuine profiles. Experimental results show that BS-SC outperforms baseline unsupervised approaches, even when prior knowledge is provided.
Collaborative recommender systems are vulnerable to shilling attacks. To address this issue, many methods including supervised and unsupervised have been proposed. However, supervised detection methods require training classifiers and they only apply to detect known types of attacks. The existing unsupervised detection methods need to know the prior knowledge of attacks, otherwise they suffer from low detection precision. In this paper, we present BS-SC, an unsupervised approach for detecting shilling profiles, which does not need to know the attack size or to label the candidate spammers. BS-SC starts from an in-depth analysis of user behaviors and uses two key mechanisms (i.e., behavior features extraction and behavior similarity matrix clustering) to distinguish shilling profiles from genuine ones. The behavior features reflect the behavior difference between genuine and shilling profiles, and the behavior similarity matrix clustering is to cluster shilling profiles based on their highly similar behaviors. Experimental results on the MovieLens and the sampled Amazon review datasets indicate that BS-SC outperforms the baseline unsupervised approaches, even when the prior knowledge is given for them.
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