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

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 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.

Gender differences using online auctions within a generation Y sample: An application of the Theory of Planned Behaviour

“…The customer-to-customer (C2C) online auction is a form of e-commerce that has become increasing popular over recent years (McLaughlin et al, 2017). This popularity has been due to several important factors such as users' perceptions of its convenience (Verhagen and Dolen, 2011;Majadi et al, 2018), competitive pricing (Zhou et al, 2007) and ease of locating products and services (Weinberg and Davis, 2005) among others. Interestingly, some users view the enjoyment and experience of the bidding process as another important motivation (Rauniar et al, 2009).…”

Collusive shill bidding detection in online auctions using Markov Random Field

“…Many researchers have proposed various proposals depending on their objectives for detecting shill bidders in online auctions (i.e., statistical (Majadi and Trevathan, 2018a;Majadi et al, 2018b;Sadaoui and Wang, 2016;Sadaoui et al, 2015;Trevathan and Read, 2007b;Trevathan and Read, 2005), agent-based trust management system (Dong et al, 2009;Ford et al, 2009;Xu et al, 2008;Patel et al, 2007), machine learning (Ganguly and Sadaoui, 2018;Tsang et al, 2014a, Tsang et al, 2014bLei et al, 2012;Yoshida and Ohwada, 2010), neural network (Ford et al, 2012;Lin et al, 2012;Goel et al, 2010), data mining (Pandit et al, 2007;Chau et al, 2006;Shah et al, 2003), etc. ).…”

“…The classifier is used to detect shill bidding and take actions if any auction found infected. Majadi et al (2018a) proposed a run-time statistical approach, referred to as the Live Shill Score (LSS) algorithm, for detecting shill bidders in online auctions. The algorithm splits an auction into four time periods and examines each bidder's bidding behaviours at set points in time.…”