Entropy | |
Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes | |
Jun-Lin Lin1  Laksamee Khomnotai1  | |
[1] Department of Information Management, Yuan Ze University, 135 Yuan-Tung Road, Chungli, Taoyuan 32003, Taiwan; | |
关键词: online auction; fraudster detection; social network analysis; | |
DOI : 10.3390/e18010011 | |
来源: mdpi | |
【 摘 要 】
Online auction websites use a simple reputation system to help their users to evaluate the trustworthiness of sellers and buyers. However, to improve their reputation in the reputation system, fraudulent users can easily deceive the reputation system by creating fake transactions. This inflated-reputation fraud poses a major problem for online auction websites because it can lead legitimate users into scams. Numerous approaches have been proposed in the literature to address this problem, most of which involve using social network analysis (SNA) to derive critical features (e.g.,
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
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RO202003190000838ZK.pdf | 499KB | download |