期刊論文
| 學年 | 100 |
|---|---|
| 學期 | 1 |
| 出版（發表）日期 | 2011-09-01 |
| 作品名稱 | A novel two-stage phased modeling framework for early fraud detection in online auctions |
| 作品名稱（其他語言） | |
| 著者 | Chang, Wen-Hsi; Chang, Jau-Shien |
| 單位 | 淡江大學資訊管理學系 |
| 出版者 | Kidlington: Pergamon |
| 著錄名稱、卷期、頁數 | Expert Systems with Applications 38(9), pp.11244–11260 |
| 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. |
| 關鍵字 | Fraud detection; Early detection; Online auction; Instance-based learning; E-commerce |
| 語言 | en |
| ISSN | 0957-4174 |
| 期刊性質 | 國外 |
| 收錄於 | SCI |
| 產學合作 | |
| 通訊作者 | Chang, Wen-Hsi |
| 審稿制度 | |
| 國別 | GBR |
| 公開徵稿 | |
| 出版型式 | 紙本 |
| 相關連結 |
機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/58503 ) |