Online Detection of Shill Bidding Fraud Based on Machine Learning Techniques

E-auctions have attracted serious fraud, such as Shill Bidding (SB), due to the large amount of money involved and anonymity of users. SB is difficult to detect given its similarity to normal bidding behavior. To this end, we develop an efficient SVM-based fraud classifier that enables auction companies to distinguish between legitimate and shill bidders. We introduce a robust approach to build offline the optimal SB classifier. To produce SB training data, we combine the hierarchical clustering and our own labelling strategy, and then utilize a hybrid data sampling method to solve the issue of highly imbalanced SB datasets. To avert financial loss in new auctions, the SB classifier is to be launched at the end of the bidding period and before auction finalization. Based on commercial auction data, we conduct experiments for offline and online SB detection. The classification results exhibit good detection accuracy and mis-classification rate of shill bidders.