Building High-Quality Auction Fraud Dataset

Given the magnitude of online auction transactions, it is difficult to safeguard consumers from dishonest sellers, such as shill bidders. To date, the application of machine learning to auction fraud detection has been limited. Shill Bidding (SB) is a severe auction fraud, which is driven by modern-day technologies and clever scammers. The difficulty of identifying the behavior of sophisticated fraudsters and the unavailability of training datasets hinder the research on SB detection. The aim of this study is to develop a high-quality SB dataset. To do so, first, we crawled and preprocessed a large number of commercial auctions and bidders' history as well. We thoroughly preprocessed both datasets to make them usable for the computation of the SB metrics. Nevertheless, this operation requires a deep understanding of the behavior of auctions and bidders. Second, we introduced two new SB patterns and implemented other existing ones. Finally, we removed outliers to improve the quality of the training fraud data.