A novel two-stage phased modeling framework for early fraud detection in online auctions

doi:10.1016/j.eswa.2011.02.172

淡江大學機構典藏 > 商管學院 > 資訊管理學系暨研究所 > 期刊論文 > Item 987654321/58503

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題名:	A novel two-stage phased modeling framework for early fraud detection in online auctions
作者:	Chang, Wen-Hsi;Chang, Jau-Shien
貢獻者:	淡江大學資訊管理學系
關鍵詞:	Fraud detection;Early detection;Online auction;Instance-based learning;E-commerce
日期:	2011-09
上傳時間:	2011-10-01 12:05:12 (UTC+8)
出版者:	Kidlington: Pergamon
摘要:	Reported dollar losses from online auction fraud were over $43M in 2008 in the US (NW3C, 2009). In general, reputation systems provided by online auction sites are the most common countermeasure available for buyers to evaluate a seller’s credit. Unfortunately, feedback score mechanisms are too easily manipulated, creating falsely overrated reputations. In addition, existing research on online auction fraud shows that a more complicated reputation management system could weaken the motivation of committing a fraud. However, very few of the previous work addresses the most important issue of a fraud detection mechanism is to discover a fraudster before he defrauds as early as possible. Therefore, developing an effective early fraud detection mechanism is necessary to prevent fraud for online auction participants. This paper proposes a novel two-stage phased modeling framework that integrates hybrid-phased models with a successive filtering procedure to identify latent fraudsters by examining the phased features of potential fraudsters’ lifecycles. This framework improves the performance of identifying latent fraudsters disguising as legitimate accounts with diverse features. In addition, a composite of measuring attributes we devised in this study is also helpful in modeling fraudulent behavior. To demonstrate the effectiveness of the proposed methods, real transaction data were collected from Yahoo!Taiwan (http://tw.bid.yahoo.com/) for training and testing. The experimental results show that the true positive rate of detecting fraudsters is over 93% on average. Furthermore, the proposed framework can significantly improve the precision and the success rate of fraud detection; the experimental results also show that the fraud detection models constructed by conventional methods are ineffective in detecting latent fraudsters.
關聯:	Expert Systems with Applications 38(9), pp.11244–11260
DOI:	10.1016/j.eswa.2011.02.172
顯示於類別:	[資訊管理學系暨研究所] 期刊論文

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