| Summary: | 碩士 === 淡江大學 === 資訊管理學系碩士班 === 99 === The online auction fraud is becoming a serious problem in recent years. In Yahoo!Taiwan, about 12,000 fraud cases have been reported in the past two years. Researchers have proposed a lot of useful detection methods to help the trader in avoiding online auction frauds. However, these methods proposed in the related work do not consider an important issue in fraud detection, that is, they are not designed for detecting latent fraudsters. In view of fraud prevention, we need recognize the fraudsters before they become fraudster actually. To detect latent fraudsters effectively, this study first proposed a set of new attributes to model the fraudsters. Then, an endpoint-backtrack method is developed to build detection models for latent fraudsters. In addition, a two-phased detection flow is designed to improve the overall accuracy. To demonstrate the effectiveness of the proposed method, we collect real transaction data from Yahoo! Taiwan auction sites and conduct a series of experiments. The results show that, in comparison with other work, our method can provide better precision and recall rate for latent fraudster detection.
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