Discovering shilling groups in a real e-commerce platform
Abstract
Purpose
With the popularity of e-commerce, shilling attack is becoming more rampant in online shopping websites. Shilling attackers publish mendacious ratings as well as reviews for promoting or suppressing target products. The purpose of this paper is to investigate group shilling, a new typed shilling attack, behavior in a real e-commerce platform (e.g. Amazon.cn).
Design/methodology/approach
Several behavioral features are proposed for modeling the shilling group, and thus an unsupervised ranking method based on principal component analysis (PCA) is presented for identifying shilling groups from real users on Amazon.cn.
Findings
As indicated by the behavior analysis, the proposed method has successfully identified a number of shilling groups on Amazon. Meanwhile, the effectiveness of the proposed features and accuracy of the proposed unsupervised method are carefully validated.
Originality/value
This paper presents a set of solutions for discovering shilling groups when the ground truth labels are hard to be obtained in real environment, including candidate groups generation, behavioral features definition and unsupervised detection.
Keywords
Acknowledgements
This research was partially supported by the National Natural Science Foundation of China (NSFC) under Grants 71571093, 71372188 and 61502222, National Center for International Joint Research on E-Business Information Processing under Grant 2013B01035, National Key Technologies R & D Program of China under Grant 2013BAH16F03, Industry Projects in Jiangsu S & T Pillar Program under Grant BE2014141, Natural Science Foundation of Jiangsu Province of China under Grant SBK2015042593 and Key Project of Natural Science Research in Jiangsu Provincial Colleges and Universities under Grant 12KJA520001, 14KJA520001, 14KJB520015, 15KJB520012 and 15KJB520011.
Citation
Wang, Y., Wu, Z., Bu, Z., Cao, J. and Yang, D. (2016), "Discovering shilling groups in a real e-commerce platform", Online Information Review, Vol. 40 No. 1, pp. 62-78. https://doi.org/10.1108/OIR-03-2015-0073
Publisher
:Emerald Group Publishing Limited
Copyright © 2016, Emerald Group Publishing Limited