Title :
Toward a Comprehensive Model in Internet Auction Fraud Detection
Author :
Zhang, Bin ; Zhou, Yi ; Faloutsos, Christos
Author_Institution :
Carnegie Mellon Univ., Pittsburgh
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.
Keywords :
Internet; belief networks; electronic commerce; fraud; telecommunication security; Internet auction; classification accuracy; fraud detection; group behavior; loopy belief propagation algorithm; network level features; nondelivery fraud; online auction Web site; reputation system; user beliefs; Belief propagation; Computer vision; Electronic commerce; Face detection; Feedback; Internet; Machine learning; Marketing and sales; Merchandise; Social network services;
Conference_Titel :
Hawaii International Conference on System Sciences, Proceedings of the 41st Annual
Conference_Location :
Waikoloa, HI
DOI :
10.1109/HICSS.2008.455