Combining ranking concept and social network analysis to detect collusive groups in online auctions

Due to the popularity of online auction markets, auction fraud has become common. Typically, fraudsters will create many accounts and then transact among these accounts to get a higher rating score. This is easy to do because of the anonymity and low fees of the online auction platform. A literature review revealed that previous studies focused on detection of abnormal rating behaviors but did not provide a ranking method to evaluate how dangerous the fraudsters are. Therefore, we propose a process which can provide a method to detect collusive fraud groups in online auctions. First, we implement a Web crawling agent to collect real auction cases and identify potential collusive fraud groups based on a k-core clustering algorithm. Second, we define a data cleaning process to remove the unrelated data. Third, we use the Page-Rank algorithm to discover the critical accounts of the groups. Fourth, we developed a ranking method for auction fraud evaluation. This method is an extension of the standard Page-Rank algorithm and combines the concepts of Web structure and risk evaluation. Finally, we conduct experiments using the Adaptive Neuro-Fuzzy Inference System (ANFIS) neural network and verify the performance of our method by applying it to real auction cases. In summary, we find that the proposed ranking method is effective in identifying potential collusive fraud groups.