The Use of Contextual Information to Detection of Fraud on Online Auctions

Currently, Internet auction portals are an integral part of business activities on the Internet. Anyone can easily participate in online auctions, either as a seller or a buyer (bidder), and the total turnover on Internet auction portals represents billions of dollars. However, the amount of fraud in these Internet auctions is related to their popularity. To prevent discovery, fraudsters exhibit normal trading behaviors and disguise themselves as honest members. It is therefore not easy to detect fraud in online auctions. There are some papers and approaches dealing with this problem with varying results. Most of them concentrate on the selection of the attributes available within online auction portals and on computational methods for their processing. This study proposes extended the fraud detection approach by using certain contextual information whose origin is outside online auctions portals. The suggested model integrates information from auctions and relevant contextual information with the aim to evaluate the behavior of certain sellers in an online auction and determine whether it is legal or not. Experimental results show that this approach based on the use of contextual information from other Internet sources provides good results and enhances significantly the accuracy of detection of certain types of fraud in online auctions.