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    Title: 線上拍賣詐騙偵測之屬性挑選與流程設計
    Other Titles: Construction for the classification feature selection and the fraud detection flow in online auctions
    Authors: 劉祐宏;Liu, Yu-Hung
    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;屬性挑選;分類樹;線上拍賣;電子商務;Fraud Detection;Attribute Selection;Decision tree;Online Auction;Electronic Commerce
    Date: 2012
    Issue Date: 2013-04-13 11:40:36 (UTC+8)
    Abstract: 隨著線上拍賣交易量的快速成長,陸續發生許多交易糾紛,其中最嚴重的莫過於詐騙。拍賣平台提供的二元名聲系統不足以保護消費者避開陷阱,有時更淪為詐騙者的行騙工具。有鑑於此,學者們紛紛提出各種詐騙偵測方法,期能協助使用者避開詐騙、安心交易。典型的做法為設計一套詐騙偵測屬性集,並使用不同的學習演算法來塑模。這些方法雖然各有特色,但很少能兼顧偵測的成本與效益,此外,相關研究多使用單一偵測模型,使其效能受到限制。為了節省偵測成本,並提升偵測準確性,本研究首先發展一套詐騙偵測屬性篩選演算法-EFCBF,期能以較少的屬性,獲得較佳的偵測結果,以降低偵測成本。根據挑選的屬性集,本研究進一步提出一套平衡式詐騙偵測流程,以互補方式結合多個偵測模型,提升總體的準確性。為了驗證提出方法的有效性,我們蒐集了Yahoo!Taiwan的交易資料進行實驗,並與前人研究比較。結果顯示EFCBF屬性挑選方法與平衡式詐騙偵測流程均能以較少的成本,提供較佳的詐騙偵測結果。最後,為了增進實用性,本研究根據上述方法,開發了一套線上拍賣決策支援工具-AuctionGuard,協助使用者挑選交易對象,及時避開詐騙與交易糾紛,提升交易的滿意度。
    With the rapid growth of online auction transaction, there have been incidents of many trade disputes, the most serious of which is fraud. The binary reputation system in online auction platform is insufficient to protect consumers avoid the trap, sometimes even become a defraud tool used by fraudsters. For this reason, scholars have proposed a variety of fraud detection methods to help users to avoid fraud to help ease transactions. The typical approach for detect fraud is the design of a fraud detection attribute set, and use different learning algorithms to build fraud detection models. Although these methods have their own characteristics, but few take into account the costs and benefits of detection, in addition, related studies using a single detection model and so that performance is limited. In order to save the detection cost and improve the detection accuracy, this research have developed a fraud detection attribute filtering algorithm-EFCBF, using fewer attributes but gain better detection results to reduce the detection cost. According to the set of selected attributes, we further proposed a balanced fraud detection process, the combination of multiple detection models in a complementary manner to enhance the overall accuracy. In order to verify the proposed method, we experiment by using the collected transaction information in Yahoo! Taiwan, and compared with the previous studies. The results show that EFCBF and balanced fraud detection process both can provide better fraud detection results at less cost. Finally, in order to enhance the practicality of this research under this approach, we developed a set of online auction decision support tools-AuctionGuard, to assist the user in the selection of trading partners in a timely manner to avoid fraud and trading disputes for improve satisfaction with the transaction.
    Appears in Collections:[資訊管理學系暨研究所] 學位論文

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