淡江大學機構典藏:Item 987654321/68467
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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/68467


    Title: Using Clustering Techniques to Analyze Fraudulent Behavior Changes in Online Auctions
    Authors: Chang, Wen-Hsi;Chang, Jau-Shien
    Contributors: 淡江大學資訊管理學系
    Date: 2010-06
    Issue Date: 2011-10-23 12:58:06 (UTC+8)
    Publisher: N.Y.: IEEE (Institute of Electrical and Electronic Engineers)
    Abstract: Schemed fraudsters often flip behavior in terms of circumstances change as camouflage for disguising malicious actions in online auctions. For instance, the fake transaction records interwoven with real trades are indistinguishable from legitimate transaction histories. The ways of fraudulent behavior changes formulate different types of tricks for swindling. To avoid trading with fraudsters, recognizing the types of fraudulent behavior changes in advance is helpful in choosing appropriate trading partners. Therefore, in order to distinguish the types of behavior changes from different fraudsters, clustering techniques were applied such as X-Means for grouping in characteristics. Afterwards, C4.5 decision trees were employed for inducing the rules of the labeled clusters. In this study, the real transaction data of 236 proven fraudsters was collected from Yahoo!Taiwan for testing. The experimental results demonstrate that the fraudsters are categorized into 4 natural groups and the vast majority of fraudster, 93% of fraudsters on average, follows certain default models to develop a scam. The findings of this study also make online auction early fraud detection possible.
    Relation: 2010 International Conference On Networking and Information Technology (ICNIT 2010), pp.34-38
    DOI: 10.1109/ICNIT.2010.5508564
    Appears in Collections:[Graduate Institute & Department of Information Management] Proceeding

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