Detecting Collusive Shill Bidding

Trevathan, Jarrod; Read, Wayne

doi:10.1109/itng.2007.74

Cited by 25 publications

(12 citation statements)

References 7 publications

(14 reference statements)

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“…In this approach they have introduced a 3 rd party interface called mediator between sellers and buyers to provide them a common interaction platform where they can communicate and cryptographic methods are used. Jarrod Trevathan and Wayne Read [8] proposed Shill Score Algorithm (SS) to detect the presence of shills in any auction by calculating a balanced score called shill score which constitutes of various defined parameters that play role in any auction. If any fraudulent activity will occur, shill score will be higher.…”

Section: Related Workmentioning

confidence: 99%

Online in-auction fraud detection using online hybrid model

Gupta

Mundra

2015

International Conference on Computing, Communication &Amp; Automation

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Section: Related Workmentioning

confidence: 99%

Online in-auction fraud detection using online hybrid model

Gupta

Mundra

2015

International Conference on Computing, Communication &Amp; Automation

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“…It might also be possible to use the latest in vendor bidding discovery software to detect suspicious, coordinated buying and selling behavior. EBay uses a software programme called ''Shill Hunter'' to detect likely vendor bidding activity (Carlton & Bensinger, 2000;Trevathan & Read, 2006).…”

Section: Identifying and Reporting Incidences Of Vendor Biddingmentioning

confidence: 99%

Vendor bidding on online auctions

Tokeley

2007

J Consum Policy

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This article examines the legal protection of consumers from vendor bidding on New Zealand online auctions sites. The current lack of legal control over online vendor bidding is contrasted with the more restrictive legal position taken in respect of vendor bidding in traditional auctions. The article argues that online vendor bidding is misleading in nature and harmful to consumers. It argues that the law is justified in intervening in order to prevent or discourage the practice. Suggestions are made for a multilayered legal approach to the problem. Copyright Springer Science+Business Media, LLC 2007Protecting consumers, Vendor bidding, Online auctions, New Zealand,

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“…al. [3], Trevathan and Read [8]). Regardless of the collection means, there appears to be no standard or documented manner in which the data collection has occurred.…”

Section: Introductionmentioning

confidence: 99%

A Software Tool for Collecting Data from Online Auctions

Balingit

Trevathan

Lee

et al. 2009

2009 Sixth International Conference on Information Technology: New Generations

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Online auctions are increasingly becoming the platform of choice for dubious sellers to engage anonymously in fraudulent behaviour. While researchers are noble in their efforts to devise mechanisms to counter auction fraud, they are often frustrated by the lack of available auction data. Such data is an invaluable tool to gain insight about fraudulent traits and for testing proposed security remedies. This is compounded by online auction sources being non-cooperative in providing auction data, usually citing "security and privacy" as reasons for not wanting to help. This paper presents a software tool that can extract data from various online auction sources. The system is able to collect all the data for a given search criteria on auctions that have completed, and also returns later to collect the data from ongoing auctions once they have completed (without user intervention). We share our experiences from the development process and describe the challenges that must be overcome to successfully set up such a system. The data collected is used to analyse the behaviour and bidding patterns of sellers and buyers that are engaged in online auctions. The work presented in this paper represents the first serious attempt at creating an openly available software tool and establishing a repository of online auction data that will be free for use by other researchers.

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Detecting Collusive Shill Bidding

Cited by 25 publications

References 7 publications

Online in-auction fraud detection using online hybrid model

Online in-auction fraud detection using online hybrid model

Vendor bidding on online auctions

A Software Tool for Collecting Data from Online Auctions

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