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    Title: Analysis of fraudulent behavior strategies in online auctions for detecting latent fraudsters
    Authors: Chang, Jau-Shien;Chang, Wen-Hsi
    Contributors: 淡江大學資訊管理學系
    Keywords: Early fraud detection;Behavior fluctuation;Clustering;Online auction;E-commerce
    Date: 2014-03-01
    Issue Date: 2014-04-28 15:13:06 (UTC+8)
    Publisher: Amsterdam: Elsevier BV
    Abstract: Online auction fraudsters constantly monitor the contextual situations of the auction and change their behavior strategies accordingly to distract the attention of their targets. This flipping of behavior makes it difficult to identify fraudsters. Thus, legitimate traders need appropriate countermeasures to avoid becoming victimized. To help online auction users detect fraudsters as early as possible, this study develops a systematic method to discover the fraudulent strategies from proven cases of online auction fraud. First, according to the results of cluster analysis on the proven fraudsters, four typical types of fraud are identified, which are Aggressive, Classical, Luxury and Low-profiled. To provide better insight, a strategy is further represented by a series of status transitions. Hidden statuses of latent fraudsters are discovered by applying X-means clustering to the phased profiles of their transaction histories. As a result, various strategies can be extracted by such a systematic method and interesting characteristics are found in these strategies. For example, about 80% fraudsters in the Yahoo!Taiwan auction site flip their behavior no more than two times, which is not as complicated as expected originally. Based on these discovered fraudulent statuses, a high-resolution fraud detection method is performed to classify suspects into legitimate users or fraudsters in different statuses, potentially improving overall detection accuracy. A two-way monitoring procedure is then proposed to successively examine the statuses of a suspicious account. Analysis shows that the two-way monitoring method is promising for better detection of well-camouflaged fraudsters.
    Relation: Electronic Commerce Research and Applications 13(2), pp.79–97
    DOI: 10.1016/j.elerap.2013.10.004
    Appears in Collections:[Graduate Institute & Department of Information Management] Journal Article

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