Toward a Comprehensive Model in Internet Auction Fraud Detection

Abstract

Fraud detection has become a common concern of the online auction Web sites. Fraudsters often manipulate reputation systems and commit nondelivery fraud. To deal with fraud in group behavior we consider network level features, such as users' beliefs of other users. In this paper we use the loopy belief propagation algorithm and apply it to network level fraud detection, classifying fraudsters, accomplices, as well as honest users. Our method shows good classification accuracy using real data.