Building High-Quality Auction Fraud Dataset
This work addresses the challenge of limited training data for shill bidding detection, which is a significant issue for online auction platforms and consumers, but it is incremental as it focuses on dataset creation rather than novel detection methods.
The study tackled the problem of detecting shill bidding fraud in online auctions by creating a high-quality dataset, which involved crawling and preprocessing auction and bidder data, introducing new fraud patterns, and removing outliers to improve data quality.
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.