Title: Scraping and Preprocessing Commercial Auction Data for Fraud Classification,
Abstract: In the last three decades, we have seen a significant increase in trading
goods and services through online auctions. However, this business created an
attractive environment for malicious moneymakers who can commit different types
of fraud activities, such as Shill Bidding (SB). The latter is predominant
across many auctions but this type of fraud is difficult to detect due to its
similarity to normal bidding behaviour. The unavailability of SB datasets makes
the development of SB detection and classification models burdensome.
Furthermore, to implement efficient SB detection models, we should produce SB
data from actual auctions of commercial sites. In this study, we first scraped
a large number of eBay auctions of a popular product. After preprocessing the
raw auction data, we build a high-quality SB dataset based on the most reliable
SB strategies. The aim of our research is to share the preprocessed auction
dataset as well as the SB training (unlabelled) dataset, thereby researchers
can apply various machine learning techniques by using authentic data of
auctions and fraud.