Combining ranking concept and social network analysis to detect collusive groups in online auctions
Expert Systems with Applications: An International Journal
Survey: Combating online in-auction fraud: Clues, techniques and challenges
Computer Science Review
Generating realistic online auction data
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Fuzzy rule optimization for online auction frauds detection based on genetic algorithm
Electronic Commerce Research
Detecting online auction shilling frauds using supervised learning
Expert Systems with Applications: An International Journal
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Shill bidding is where spurious bids are introduced into an auction to drive up the final price for the seller, thereby defrauding legitimate bidders. Trevathan and Read presented an algorithm to detect the presence of shill bidding in online auctions. The algorithm observes bidding patterns over a series of auctions, and gives each bidder a shill score to indicate the likelihood that they are engaging in shill behaviour. While the algorithm is able to accurately identify those with suspicious behaviour, it is designed for the instance where there is only one shill bidder. However, there are situations where there may be two or more shill bidders working in collusion with each other. Colluding shill bidders are able to engage in more sophisticated strategies that are harder to detect. This paper proposes a method for detecting colluding shill bidders, which is referred to as the collusion score. The collusion score, either detects a colluding group, or forces the colluders to act individually like a single shill, in which case they are detected by the shill score algorithm. The collusion score has been tested on simulated auction data and is able to successfully identify colluding shill bidders.