English  |  正體中文  |  简体中文  |  Items with full text/Total items : 52052/87180 (60%)
Visitors : 8895325      Online Users : 109
RC Version 7.0 © Powered By DSPACE, MIT. Enhanced by NTU Library & TKU Library IR team.
Scope Tips:
  • please add "double quotation mark" for query phrases to get precise results
  • please goto advance search for comprehansive author search
  • Adv. Search
    HomeLoginUploadHelpAboutAdminister Goto mobile version
    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/52136

    Title: 一套線上拍賣詐騙即時偵測系統
    Other Titles: A real-time fraud detection system for online auctions
    Authors: 梁賀翔;Liang, He-siang
    Contributors: 淡江大學資訊管理學系碩士班
    張昭憲;Chang, Jau-shien
    Keywords: 詐騙偵測;線上拍賣;分類樹;電子商務;Fraud Detection;Online Auction;Decision Trees;e-commerce
    Date: 2010
    Issue Date: 2010-09-23 16:53:57 (UTC+8)
    Abstract: 本研究發展了一套具早期預警功能之線上拍賣詐騙即時偵測系統AntiFraud,當使用者以瀏覽器瀏覽商品網頁時,AntiFraud便同時啟動,進而主動判別該商品的賣方為詐騙者之可能性,以減少使用者遭受詐騙的機會。系統核心部分,整合了混合階段模型與分階段模型兩類塑模方式,用以擷取詐騙者在各階段的行為特徵來達成早期預警的功效。
    This study proposes a real-time online auction fraud detection system named as AntiFraud. As a user executes Firefox to browse commodity pages, AntiFraud will be activated simultaneously to detect the auctioneers for reducing the probability of being defrauded. The kernel of the system consists of detection models by hybrid phased modeling and single phased modeling. The integration of the previous two types of modeling methods is to extract the comprehensive fraudulent features in different phases for achieving the capability of early warning in online auctions.
    To enhance the capability of fraud detection, AntiFraud has implemented a 2-level detection procedure for identifying fraudsters and predicting in which current phase they stay most probably. In addition, the system induces judgment rules of identifying fraudsters from the results of learning processes. In this study, we collected fraudulent transaction histories occurred at Yahoo-Taiwan during 2007-2009 for analyzing the trend of fraud scheme evolution. According to the changes of fraudulent behavior, we construct new behavior models against the new types of frauds as early as possible. To validate the effectiveness of AntiFraud, we also downloaded real data from Yahoo- Taiwan for testing. Our experimental results present the practicality of the system including both the accuracy of fraud detection and the capability of early warning.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

    Files in This Item:

    File SizeFormat

    All items in 機構典藏 are protected by copyright, with all rights reserved.

    DSpace Software Copyright © 2002-2004  MIT &  Hewlett-Packard  /   Enhanced by   NTU Library & TKU Library IR teams. Copyright ©   - Feedback