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A Real-Time Detection Algorithm for Identifying Shill Bidders in Multiple Online Auctions
|Title:||A Real-Time Detection Algorithm for Identifying Shill Bidders in Multiple Online Auctions|
|Keywords:||Social Shopping: The Good, the Bad and the Ugly|
Auction fraud, Bidding behavior, Online auction, Live Shill Score, Shill bidding.
|Date Issued:||03 Jan 2018|
|Abstract:||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.|
|Rights:||Attribution-NonCommercial-NoDerivatives 4.0 International|
|Appears in Collections:||
Social Shopping: The Good, the Bad and the Ugly|
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