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

Abstract

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.

Online Auction Fraud Detection in Privacy-Aware Reputation Systems

“…[11] neighbor diversity [13] neighbor driven attributes [12] 3. Privacy-Aware Reputation System and Privacy-Related Attributes…”

“…Consequently, if a third party crawls an auction website to build the rating network of auction accounts (see Section 2.2), the transactions within a collusive group of auction accounts may be hidden and thus cannot be reconstructed. Because many fraud detection approaches [1][2][3][4][5][6][7][8][9][10][11][12][13] employ the rating network to detect fraudsters, anonymous transactions in the reputation system may render these approaches unfeasible to detect fraudsters who take advantage of anonymous transactions. To the best of our knowledge, most fraud detection approaches are based on datasets crawled from auction websites instead of datasets directly provided by the auction websites.…”

“…A subset of this dataset was also used in our previous work [12,13]. The data collection process proceeded in a level-by-level manner [4,8,10,11,29] and is explained as follows:…”

“…The fourth set contained only one attribute, ND r (neighbor diversity on the number of received ratings [13]). The fifth set contained only one attribute, NDA mean (the mean of the numbers of received ratings of the node's neighbors [12]). In this performance study, we tested these five sets of attributes and then evaluated whether their performance can be improved by adding R a and n ab .…”

“…Most recent approaches for detecting inflated reputation fraud are based on Social Network Analysis (SNA) [1][2][3][4][5][6][7][8][9][10][11][12][13]. These SNA-based approaches construct a social network of buyers and sellers based on their past transactions, and then detect fraudsters by finding cohesive groups in the network.…”