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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/87500

    Title: Effective fraud detection in online auctions
    Other Titles: 線上拍賣詐騙之有效偵測
    Authors: 張文熙;Chang, Wen-Hsi
    Contributors: 淡江大學管理科學學系博士班
    張昭憲;Chang, Jau-Shien
    Keywords: 詐騙偵測;早期預警;線上拍賣;分類法;電子商務;Fraud Detection;Early Warning;Online Auction;classification;e-commerce
    Date: 2013
    Issue Date: 2013-04-13 11:16:31 (UTC+8)
    Abstract: 近年來,線上拍賣的蓬勃發展有目共睹,儼然成為電子商務最成功的典範之一。然而,龐大的商業利益也引起許多非法人士的注意,詐騙者開始利用各種偽裝來詐取財物,讓消費者防不勝防。長此以往,將嚴重影響線上拍賣的交易安全,並限制其未來發展。有鑒於此,本研究發展出一系列有效的拍賣詐騙早期偵測方法,希望在詐騙發生前便能對使用者提出警訊。此外,為了提升這些方法的實用性,也針對成本考量,提出了多種不同的偵測模式。這些方法的特點如下:
    (1) 針對早期預警的需求,本論文提出階段性特徵描述法(phased profiling),在塑模之前,以不同方法切割交易歷史。如此,便可呈現詐騙者在潛伏期不同階段的行為特徵,並有利於詐騙行為變化之觀察。
    (2) 考量詐騙者在潛伏期可能展現不同行為,本研究發展出混合階段塑模法(hybrid phased modeling),以提升早期偵測的效能。有別於傳統方法,混合塑模排除詐騙者在爆發期的交易紀錄,萃取其在潛伏期不同階段的行為特徵,混合之後再行塑模,以加強早期詐騙之偵測能力。同時,我們設計出兩階段偵測流程,以更精細的方式檢驗可疑帳號,進一步提升準確性。
    (3) 為降低偵測成本,本研究以前人研究提出的詐騙指標為基礎,利用改良式wrapper approach篩選出一組精簡的指標集,再以此為基礎,建立偵測模型。搭配精簡指標集,本研究進一步發展補集塑模法(complement phased modeling),以更少量的交易紀錄來塑模。結果不僅提高偵測準確率,同時也減少資料下載的負荷,有利於低成本、高效能之偵測系統的發展。
    (4) 為了瞭解詐騙者行為的演進,本研究利用分群技術(clustering)與階段行為描述來進行分析。分析結果不僅對詐騙行為有更進一步的了解,也有助於發展新的詐騙偵測系統,並加強更詐騙偵測的解析能力。
    In recent years, online auction has become one of the most successful business models; however, the tremendous profit also appeals to many fraudsters. Schemed fraudsters camouflage their malicious intent to distract customers for profit, seriously threatening online auction security. This dissertation aims to develop a set of methods for constructing an effective early fraud detection system. This research proposes various detection methods taking detection cost into account to enhance the practicality of such a system, including the following:
    (1) To satisfy the need of early fraud detection, a phased profiling approach partitions the transaction histories of traders before detection model construction. The latent behavior of uncovered fraudsters can be extracted from these segmented transaction histories presenting different periods of lifespan that is helpful in observing fraudulent behavior fluctuation.
    (2) To address the diversity of latent behavior, a hybrid phased modeling method increases the detection accuracy for latent fraudsters. This method extracts features from different phases of the latency period to construct models for enhancing the capability of early fraud detection. To further improve accuracy, a two-stage detection procedure uses various detection models to carefully examine the behavior of a suspicious account.
    (3) To reduce detection costs, a modified wrapper approach is used to select a concise set of measured attributes, which is then used to construct the model. In addition, a complement phased modeling method increases the accuracy while facilitating the data downloading from the auction site, providing a cost-effective detection procedure.
    (4) To analyze the evolution of fraudulent behavior, clustering methods incorporated with phased profiling are used to classify the types of fraudsters. This analysis helps to parse fraudulent behavior with greater granularity and resolution.
    To test the effectiveness of the methods we proposed, real transaction records were collected from Yahoo!Taiwan. The proposed methods not only improve the accuracy of fraud detection but can also identify latent fraudsters, a necessary requirement for early detection. The results show that these methods improve the practicality of fraud detection system, allowing online auction participants and the trading environment to be secured in a cost-effective way.
    Appears in Collections:[管理科學學系暨研究所] 學位論文

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