In this study, we developed a high-quality SB dataset. The difficulty of identifying the behavior of sophisticated fraudsters and the unavailability of training datasets hinder the research on SB detection. Shill Bidding (SB) is a severe auction fraud, which is driven by modern-day technologies and clever scammers. To date, the application of Machine Learning Techniques (MLTs) to auction fraud has been limited, unlike their applications for combatting other types of fraud. Given the magnitude of online auction transactions, it is difficult to safeguard consumers from dishonest sellers, such as shill bidders. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities. Since shill bidding datasets are imbalanced, we assess advanced over-sampling, under-sampling and hybrid-sampling methods and compare their performances based on several classification algorithms. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach.
For our study, we employ an actual training dataset, but the data are unlabeled. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day.
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