A Multiple-Phased Modeling Method to Identify Potential Fraudsters in Online Auctions
學年 98
學期 2
發表日期 2010-05-07
作品名稱 A Multiple-Phased Modeling Method to Identify Potential Fraudsters in Online Auctions
作品名稱（其他語言）
著者 張文熙; 張昭憲
作品所屬單位 淡江大學資訊管理學系
出版者 IEEE
會議名稱 Computer Research and Development, 2010 Second International Conference on
會議地點 Kuala Lumpur, Malaysia
摘要 Loopholes in online auction sites enabled fraudsters to easily hide themselves. To reduce the odds of being defrauded, online auction traders usually use reputation systems for estimating a trading partner's credit. However, reported dollar losses of online auction fraud have hit recorded height for years that implies existing reputation systems may not prevent fraud effectively as expected. To reduce the risk of being defrauded, an ideal fraud detection mechanism should be not only to identify current fraudster but also potential ones. Therefore, this paper proposes a multiple-phased modeling method integrating with decision trees for enhancing the capability of fraud detection. To demonstrate the effectiveness of the proposed method, real transaction data were collected from Yahoo! Taiwan for training and testing. The experimental results show that the recall rate of identifying a potential fraudster before transitioning into his criminal phase was up to 86%.
關鍵字
語言 en
收錄於
會議性質 國際
校內研討會地點
研討會時間 20100507~20100510
通訊作者
國別 MYS
公開徵稿 Y
出版型式 紙本
出處 Computer Research and Development, 2010 Second International Conference on,pp.186 - 190
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