A Real-time Fraud Detection System for Online Auctions

• Main Authors: He-Siang Liang, 梁賀翔
• Other Authors: Jau-Shien Chang
• Format: Others
• Language: zh-TW
• Published: 2010
• Online Access: http://ndltd.ncl.edu.tw/handle/76387747637016807722

碩士 === 淡江大學 === 資訊管理學系碩士班 === 98 === This study proposes a real-time online auction fraud detection system named as AntiFraud. As a user executes Firefox to browse commodity pages, AntiFraud will be activated simultaneously to detect the auctioneers for reducing the probability of being defrauded. The kernel of the system consists of detection models by hybrid phased modeling and single phased modeling. The integration of the previous two types of modeling methods is to extract the comprehensive fraudulent features in different phases for achieving the capability of early warning in online auctions. To enhance the capability of fraud detection, AntiFraud has implemented a 2-level detection procedure for identifying fraudsters and predicting in which current phase they stay most probably. In addition, the system induces judgment rules of identifying fraudsters from the results of learning processes. In this study, we collected fraudulent transaction histories occurred at Yahoo-Taiwan during 2007-2009 for analyzing the trend of fraud scheme evolution. According to the changes of fraudulent behavior, we construct new behavior models against the new types of frauds as early as possible. To validate the effectiveness of AntiFraud, we also downloaded real data from Yahoo- Taiwan for testing. Our experimental results present the practicality of the system including both the accuracy of fraud detection and the capability of early warning.