Combating online in-auction fraud: Clues, techniques and challenges

Dong, Fei; Shatz, Sol M.; Xu, Haiping

doi:10.1016/j.cosrev.2009.09.001

Cited by 52 publications

(38 citation statements)

References 38 publications

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“…For recent surveys on online auction frauds in general, please refer to [18,19]. For problems associated with the online reputation system, please refer to [20,21].…”

Section: Related Workmentioning

confidence: 99%

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Lin

Khomnotai

2014

Entropy

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Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight.

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“…For recent surveys on online auction frauds in general, please refer to [18,19]. For problems associated with the online reputation system, please refer to [20,21].…”

Section: Related Workmentioning

confidence: 99%

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Lin

Khomnotai

2014

Entropy

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“…For a brief review of recent fraudster detection approaches, please refer to [7]. For surveys on online auction frauds in general, please refer to [11,12].…”

Section: Sna For Fraudster Detectionmentioning

confidence: 99%

Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes

Lin

Khomnotai

2015

Entropy

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Abstract:Online auction websites use a simple reputation system to help their users to evaluate the trustworthiness of sellers and buyers. However, to improve their reputation in the reputation system, fraudulent users can easily deceive the reputation system by creating fake transactions. This inflated-reputation fraud poses a major problem for online auction websites because it can lead legitimate users into scams. Numerous approaches have been proposed in the literature to address this problem, most of which involve using social network analysis (SNA) to derive critical features (e.g., k-core, center weight, and neighbor diversity) for distinguishing fraudsters from legitimate users. This paper discusses the limitations of these SNA features and proposes a class of SNA features referred to as neighbor-driven attributes (NDAs). The NDAs of users are calculated from the features of their neighbors. Because fraudsters require collusive neighbors to provide them with positive ratings in the reputation system, using NDAs can be helpful for detecting fraudsters. Although the idea of NDAs is not entirely new, experimental results on a real-world dataset showed that using NDAs improves classification accuracy compared with state-of-the-art methods that use the k-core, center weight, and neighbor diversity.

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“…D-S theory (Shafer, 1976), a probabilistic reasoning technique, was designed to deal with uncertainty and incompleteness of available information. Dong et al (2009) proposed a formal approach to verifying shill bidders using D-S theory (Shafer, 1976).…”

Section: Dempster-shafer Theorymentioning

confidence: 99%

“…While shilling is recognized as a problem (Bhargava et al, 2005), established means of defence against shills did not work in live auctions and did not focus on some important shill bidding behaviours (Chau et al, 2009;Dong et al, 2009), for example, multiple consecutive bidding by the same user, bidding with different identities. The advent of online auctions such as eBay, Amazon and ubid has made shill bidding much more exploitable.…”

Section: Introductionmentioning

confidence: 99%

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A fraud detection tool in E-auctions

Kavu

Rugube

Kawondera

et al. 2016

Afr. J. Math. Comput. Sci. Res.

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Due to rapid growth of the use of online auctions, fraudsters have taken advantage of these platforms to participate in their own auctions in order to raise prices (a practice called shilling). Innocent bidders have been forced to pay higher prices than they were willing to offer. This has resulted in the need to design and implement a shill detection algorithm. To eliminate this shilling problem, we designed a shilling detection algorithm integrated with an online auction. The algorithm proved to be effective and it was tested on the internet, and the short time of shill detection proved that the algorithm can work real time on e-auctions with large user base. This method can be used as a technique to eliminate shilling.

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Combating online in-auction fraud: Clues, techniques and challenges

Cited by 52 publications

References 38 publications

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Improving Fraudster Detection in Online Auctions by Using Neighbor-Driven Attributes

A fraud detection tool in E-auctions

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