Discovering shilling groups in a real e-commerce platform

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.