Abstract
Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. 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. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. For our study, we employ an actual training dataset, but the data are unlabeled. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach. 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. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities.
Highlights
This section presents the problem statement and scope as well as our research contributions about the detection of bidding fraud in commercial auctions.1.1 Problem and MotivationOver the last twenty years, the use of online auctions has rapidly increased in numerous domains, such as antiques, vehicles, and real estate
We evaluate the misclassification rates of the fraud class based on False Negative Rate (FNR) and False Positive Rate (FPR) where FNR denotes the percentage of suspicious bidders incorrectly identified, and FPR is the percentage of normal bidders incorrectly classified
One such risk is Shill Bidding (SB), which is a type of fraud wherein the price of products is inflated in an unethical manner
Summary
We present the problem statement and scope as well as our research contributions about the detection of bidding fraud in commercial auctions.1.1 Problem and MotivationOver the last twenty years, the use of online auctions has rapidly increased in numerous domains, such as antiques, vehicles, and real estate. We present the problem statement and scope as well as our research contributions about the detection of bidding fraud in commercial auctions. Since 2002, several commercial auction companies, such as Trade Me and eBay, have become immensely popular [7], [22]. Given this surge in popularity, it is perhaps unsurprising that the FBI’s Internet Crime Complaint Center reports that auction fraud has become one of the top forms of cyber-crime (Site 1). The present research focuses on Shill Bidding (SB) because, unlike the other two types of fraud, it does not leave any obvious evidence. The presence of shills will discourage honest users from participating in online auctions, and this may in turn negatively affect the auctioning business
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: Journal of theoretical and applied electronic commerce research
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
