Shill bidding occurs when fake bids are introduced into an auction on the seller's behalf in order to artificially inflate the final price. This is typically achieved by the seller having friends bid in her auctions, or the seller controls multiple fake bidder accounts that are used for the sole purpose of shill bidding. We previously proposed a reputation system referred to as the Shill Score that indicates how likely a bidder is to be engaging in price inflating behaviour with regard to a specific seller's auctions. A potential bidder can observe the other bidders' Shill Scores, and if they are high, the bidder can elect not to participate as there is some evidence that shill bidding occurs in the seller's auctions. However, if a seller is in collusion with other sellers, or controls multiple seller accounts, she can spread the risk between the various sellers and can reduce suspicion on the shill bidder. Collusive seller behaviour impacts one of the characteristics of shill bidding the Shill Score is examining, therefore collusive behaviour can reduce a bidder's Shill Score. This paper extends the Shill Score to detect shill bidding where multiple sellers are working in collusion with each other. We propose an algorithm that provides evidence of whether groups of sellers are colluding. Based on how tight the association is between the sellers and the level of apparent shill bidding is occurring in the auctions, each participating bidder's Shill Score is adjusted appropriately to remove any advantages from seller collusion. Performance has been tested using simulated auction data and experimental results are presented.
 Highlights? A Web crawling agent is used to collect real auction cases. ? The Page-Rank algorithm is used to discover the critical accounts of the groups. ? We developed a ranking method for auction fraud evaluation. ? The ANFIS neural network with real auction cases was implemented in the experiments. ? We find that the proposed ranking method is effective in identifying potential collusive fraud groups. 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. Shill bidding has become a serious issue for innocent bidders with the growing popularity of online auctions. In this paper, we study the relationship between final prices of online auctions and shill activities. We conduct experiments on real auction data from eBay to examine the hypotheses that state how the difference between final auction price and expected auction price implies shill bidding. In the experiments, a neural network based approach is used to learn the expected auction price. In particular, we trained the Large Memory Storage and Retrieval (LAMSTAR) Neural Network based on features extracted from item descriptions, listings and other auction properties. The likelihood of shill bidding is determined by a previously proposed shill certification technique based on Dempster-Shafer theory. By employing the chi-square test of independence and logistic regression, the experimental results indicate that a higher-than-expected final auction price might be used as direct evidence to distinguish likely shill-infected auctions from trustworthy auctions, allowing for more focused evaluation of shill-suspected auctions. As such, this work contributes to providing a feasible way to identify suspicious auctions that may contain shill biddings. It may also help to develop trustworthy auction houses with shill detection services that can protect honest bidders and benefit the auction markets in both the short-term and long term. We present a shilling behavior detection and verification approach for online auction systems. Assuming a model checking technique to detect shill suspects in real-time, we focus on how to verify shill suspects using Dempster-Shafer theory of evidence. To demonstrate the feasibility of our approach, we provide a case study using real eBay auction data. The analysis results show that our approach can detect shills and that using Dempster-Shafer theory to combine multiple sources of evidence of shilling behavior can reduce the number of false positive results that would be generated from a single source of evidence. Online auctions have become a quite popular and effective approach in the Internet-based eMarketplace. In concurrent auctions, where multiple auctions for identical items are running simultaneously, users’ bidding behaviors become very complicated. This situation motivates shilling behaviors, in which a seller disguises himself as normal bidders in order to drive up the bidding price and make the winning bidder pay more for an auctioned item. The goal of this paper is to propose a formal approach to verifying bidding behaviors, and especially, detecting shilling behaviors in concurrent online auctions. We develop a model template for concurrent auctions and derive auction models based on auction data from two concurrent auctions. The auction model can be formally verified using the SPIN model checker for certain behavioral properties, which are specified in pattern-based LTL (Linear Temporal Logic) formulas. To illustrate the feasibility and effectiveness of our approach, we provide a case study to show how possible shill bidders can be detected.