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  • A Run-Time Algorithm for Detecting Shill Bidding in Online Auctions

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    MajadiPUB5988.pdf (1.813Mb)
    Author(s)
    Majadi, Nazia
    Trevathan, Jarrod
    Gray, Heather
    Griffith University Author(s)
    Trevathan, Jarrod
    Year published
    2018
    Metadata
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    Abstract
    Online 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 ...
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    Online 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.
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    Journal Title
    Journal of Theoretical and Applied Electronic Commerce Research
    Volume
    13
    Issue
    3
    DOI
    https://doi.org/10.4067/S0718-18762018000300103
    Copyright Statement
    Copyright remains with the author(s) 2018 . For information about this journal please refer to the publisher’s website or contact the author(s). Articles published in JTAER are open access and distributed under a Creative Commons Attribution License
    Subject
    Information systems
    Other information and computing sciences
    Other information and computing sciences not elsewhere classified
    Marketing
    Auction fraud
    Bidding behaviour
    Live shill score
    Online auction
    Post-filtering process
    Shill bidding
    Publication URI
    http://hdl.handle.net/10072/381662
    Collection
    • Journal articles

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