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.
espanolAuction fraud; Bidding behaviour; Live shill score; Online auction; Post-filtering process; Shill bidding. EnglishOnline auctions are a popular and convenient way to engage in ecommerce. However, the amount of auction fraud has increased with the rapid surge of users participating in online auctions. Shill bidding is the most prominent type of auction fraud where a seller submits bids to inflate the price of the item without the intention of winning. Mechanisms have been proposed to detect shill bidding once an auction has finished. However, if the shill bidder is not detected during the auction, an innocent bidder can potentially be cheated by the end of the auction. Therefore, it is essential to detect and verify shill bidding in a running auction and take necessary intervention steps accordingly. This paper proposes a run-time statistical algorithm, referred to as the Live Shill Score, for detecting shill bidding in online auctions and takes appropriate actions towards the suspected shill bidders (e.g., issue a warning message, suspend the auction, etc.). The Live Shill Score algorithm also uses a Post-Filtering Process to avoid misclassification of innocent bidders. Experimental results using both simulated and commercial auction data show that our proposed algorithm can potentially detect shill bidding attempts before an auction ends. Online auctions are vulnerable to shill bidders, who intend to artificially raise bidding prices, causing winning bidders to pay more than they should pay for auctioned items. Detection of such fraudulent behaviors is very difficult, especially when an auction is in progress, or “live”. This paper focuses on a formal technique to detect shilling behaviors in live online auctions. We define a monitoring agent that can continuously watch for abnormal bidding behaviors of a monitored bidder. To make the detection process efficient, we introduce a dynamic auction model (DAM), and use real-time model checking techniques to verify shilling behaviors specified in linear temporal logic (LTL). Finally, we present an algorithm for real-time shill detection, and use a case study to demonstrate the efficiency and effectiveness of our approach. Monitoring the progress of auctions for fraudulent bidding activities is crucial for detecting and stopping fraud during runtime to prevent fraudsters from succeeding. To this end, we introduce a stage-based framework to monitor multiple live auctions for In-Auction Fraud (IAF). Creating a stage fraud monitoring system is different than what has been previously proposed in the very limited studies on runtime IAF detection. More precisely, we launch the IAF monitoring operation at several time points in each running auction depending on its duration. At each auction time point, our framework first detects IAF by evaluating each bidder's stage activities based on the most reliable set of IAF patterns, and then takes appropriate actions to react to dishonest bidders. We develop the proposed framework with a dynamic agent architecture where multiple monitoring agents can be created and deleted with respect to the status of their corresponding auctions (initialized, completed or cancelled). The adoption of dynamic software architecture represents an excellent solution to the scalability and time efficiency issues of IAF monitoring systems since hundreds of live auctions are held simultaneously in commercial auction houses. Every time an auction is completed or terminated, the participants' fraud scores are updated dynamically. Our approach enables us to observe each bidder in each live auction and manage his fraud score as well. We validate the IAF monitoring service through commercial auction data. We conduct three experiments to detect and react to shill-bidding fraud by employing datasets acquired from auctions of two valuable items, Palm PDA and XBOX. We observe each auction at three-time points, verifying the shill patterns that most likely happen in the corresponding stage for each one. Online auctions are highly susceptible to fraud. Shill bidding is where a seller introduces fake bids into an auction to drive up the final price. If the shill bidders are not detected in run-time, innocent bidders will have already been cheated by the time the auction ends. Therefore, it is necessary to detect shill bidders in real-time and take appropriate actions according to the fraud activities. This paper presents a real-time shill bidding detection algorithm to identify the presence of shill bidding in multiple online auctions. The algorithm provides each bidder a Live Shill Score (LSS) indicating the likelihood of their potential involvement in price inflating behavior. The LSS is calculated based on the bidding patterns over a live auction and past bidding history. We have tested our algorithm on data obtained from a series of realistic simulated auctions and also commercial online auctions. Experimental results show that the real-time detection algorithm is able to prune the search space required to detect which bidders are likely to be potential shill bidders. In spite of many advantages of online auctioning, serious frauds menace the auction users' interests. Today, monitoring auctions for frauds is becoming very crucial. We propose here a generic framework that covers real-time monitoring of multiple live auctions. The monitoring is performed at different auction times depending on fraud types and auction duration. We divide the real-time monitoring functionality into threefold: detecting frauds, reacting to frauds, and updating bidders' clusters. The first task examines in run-time bidding activities in ongoing auctions by applying fraud detection mechanisms. The second one determines how to react to suspicious activities by taking appropriate run-time actions against the fraudsters and infected auctions. Finally, every time an auction ends, successfully or unsuccessfully, participants' fraud scores and their clusters are updated dynamically. Through simulated auction data, we conduct an experiment to monitor live auctions for shill bidding. The latter is considered the most severe fraud in online auctions, and the most difficult to detect. More precisely, we monitor each live auction at three time points, and for each of them, we verify the shill patterns that most likely happen.