Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Abstract

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.

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

“…Therefore, several studies have focused on detecting cohesive groups in social networks [3][4][5][6]. By contrast, a recent study [7] suggested that the neighbors of a fraudster in a social network exhibit similar behavior leading to low neighbor diversity. Thus, the concept of neighbor diversity was proposed for fraudster detection.…”

“…Although the center weight improves the precision of fraudster detection, it reduces the recall [4]. Lin and Khomnotai [7] suggested that the neighbors of fraudsters in social networks exhibit similar patterns because fraudsters need their collusive neighbors to give them positive ratings in the reputation system. Thus, the concept of neighbor diversity was proposed for fraudster detection, where nodes with low neighbor diversity are likely to be fraudsters.…”

“…For example, user-level features such as the median, sum, mean, or standard deviation of the prices of merchandise that a user bought or sold within a certain period were used in [8,9]; transaction-related features (including price, frequency, comment, and connectedness in the transaction network) and user-level features (including reputation and age) were used in [10]. 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].…”

“…In brief, an NDA is an attribute of a node that is calculated using a user-selected feature of the neighbors of the node. We present several NDAs that are calculated as maximum or mean of neighbors' feature values, based on features such as the k-core, the number of received ratings, and the number of canceled transactions [3,5,7]. The objective of this study is to extend the neighbor diversity to NDAs such that the shortcomings of the neighbor diversity are avoided and the performance of fraudster detection is improved.…”

“…Numerous approaches have been proposed in the literature to detect inflated-reputation fraud [3][4][5][6][7][8][9]. Most approaches build a social network of users based on their transaction history, in which nodes and links represent users and transactions, respectively.…”