  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 Techniques (MLTs) to auction fraud has been
limited, unlike their applications for combatting other types of fraud. 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. In this study, we developed 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 pattern s and
implemented other existing SB patterns. Finally, we removed outliers to improve
the quality of training SB data.
