Shill bidding involves a seller introducing fake bids into her own online auction to force an innocent bidder to pay an inflated price. Detecting shill bidders becomes increasingly difficult if a seller colludes with other vendors or controls multiple seller accounts to perpetrate the scam. Multiple seller collusive shill bidding distributes the fraud onus among the conspiring sellers to reduce suspicion on any particular individual seller account. There are existing proposals that examine a series of auctions for evidence of collusive seller and bidder behaviour. However, if shill bidding is not detected while an auction is underway, the victim will likely face monetary loss – as it may take some time before the offending parties are traced. Therefore, it is desirable to extend multiple seller collusive shill bidding techniques to operate in real-time during an auction to prevent the fraud from running its course. This paper proposes a real-time algorithm utilising social network metrics to identify fraudulent seller accounts involved with multiple seller collusive shill bidding practices. The algorithm builds a social network graph from the auction data set to analyse suspicious interactions between sellers and bidders. Auction participants are given a social rating based on their associations. Behaviour is examined during different stages of an auction to produce evidence of collusive shill bidding. Experimental analysis on a synthetically generated auction data set shows that the algorithm can identify in real-time potential parties involved in multiple seller collusive shill bidding.

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