Auction Fraud Classification Based on Clustering and Sampling Techniques

Abstract

Online auctions created a very attractive environment for dishonest moneymakers who can commit different types of fraud. Shill Bidding (SB) is the most predominant auction fraud and also the most difficult to detect because of its similarity to usual bidding behavior. Based on a newly produced SB dataset, in this study, we devise a fraud classification model that is able to efficiently differentiate between honest and malicious bidders. First, we label the SB data by combining a hierarchical clustering technique and a semi-automated labeling approach. To solve the imbalanced learning problem, we apply several advanced data sampling methods and compare their performance using the SVM model. As a result, we develop an optimal SB classifier that exhibits very satisfactory detection and low misclassification rates.