淡江大學覺生紀念圖書館 (TKU Library)
進階搜尋


下載電子全文限經由淡江IP使用) 
系統識別號 U0002-2506201421004400
中文論文名稱 一套有效率的複合式線上拍賣詐騙偵測系統
英文論文名稱 An effective composite fraud detection system for on-line auctions
校院名稱 淡江大學
系所名稱(中) 資訊管理學系碩士班
系所名稱(英) Department of Information Management
學年度 102
學期 2
出版年 103
研究生中文姓名 林敬堯
研究生英文姓名 Ching-Yao Lin
學號 601630048
學位類別 碩士
語文別 中文
口試日期 2014-06-21
論文頁數 48頁
口試委員 指導教授-張昭憲
委員-梁德馨
委員-侯永昌
中文關鍵字 詐騙偵測  分類樹  線上拍賣  電子商務 
英文關鍵字 Fraud Detection  Decision Tree  Online Auction  Electronic Commerce 
學科別分類
中文摘要 近年來,線上拍賣的蓬勃發展有目共睹,交易量屢創新高。但在此同時,詐騙者開始進入此一便利的交易平台,利用網路的隱蔽性大肆進行不法活動。詐騙手法不斷推陳出新,甚至配合早被遺忘的陳年手法,不斷循環運用,讓有經驗的交易者也難以識破。為了識破偽裝完善的狡猾詐騙者,本研究提出了二套不同偵測方法: 分群匹配塑模法與名聲因子分類組合法,以不同的方式組合多種不同的偵測模型,以提昇詐騙偵測的準確率。在分群匹配塑模法中,訓練資料集事先以群集演算進行分群,再根據待測帳號的特性,即時建構出最合適的分類模型。名聲因子分類組合法則根據常用的拍賣者名聲模型,將分類模型分為年份、評價分數與交易類別等三種,並以協力的方式來過濾詐騙者,透過投票或權重組合來判別可疑者的身分。為了驗證方法的有效性,我們使用YAHOO!奇摩的真實交易資料進行實驗。結果顯示,本研究提出的方法能有效提升詐騙者偵測的精度,並保持優良的總體偵測成功率。
英文摘要 In recent years, the rapid growth of online auctions were seen by everyone. Trading volume hit record highs. But in the meantime, began to enter a fraudster convenient trading platform, using a network of hidden wantonly engaged in illegal activities. Scams constantly, even with long-forgotten vintage approach, continuous cycle of use , so that experienced traders also difficult to see through . Although many online auction platform precautions , but most of its design -oriented seller by the buyer cheated in order to begin to remedy the cause scammers have nothing to fear , rampant . In order to see through the disguise perfect cunning scammers , this study combined in different ways in many different classification tree , more accurate fraud detection. Type Total Year , evaluation scores and transaction types , three classification tree to generalize traders at different times of the various characteristics of different types of transactions . The classification tree is not used alone , but in a third way to filter fraudster . Combination of various ways depending on the type of classification trees to re- vote or the right to determine the combination of suspicious persons identity. In order to verify the effectiveness of the method , we use YAHOO! Kimo 's real transaction data to validate the experimental results show that our proposed method can effectively improve the accuracy of detection scammers and maintaining excellent overall detection success rate.
論文目次 目錄
第一章 前言 1
第二章 相關技術與背景知識 7
2.1 拍賣(Auctions) 7
2.2 詐騙(Fraud) 8
2.3 詐騙偵測 11
2.4 資料蒐集: 拍賣網站交易歷史記錄的下載 13
2.5 建立詐騙偵測模型 14
第三章 以多模型為基礎之詐騙偵測方法 16
3.1 分群匹配塑模法 16
3.2 名聲因子分類組合法 19
3.2.1 時間因子: 以年份作為區分 20
3.2.2 評價因子: 以總評價數作為區分 21
3.2.3 類別因子: 以商品類別作為區分 22
3.2.4 多個偵測模型的組合 23
3.3 偵測屬性集 25
第四章 實驗結果 27
4.1 實驗設定 27
4.2 效能驗證 28
參考文獻 37
附錄A 按年份塑模的加權投票 41
附錄B 按總評價數區間塑模的加權投票 43
附錄C 分群匹配塑模法實驗數據 45
附錄D 平衡式詐騙偵測驗證實驗數據 47


表目錄
表3-1評價區間表 22
表3-2 屬性集 26
表4-1 指標說明表 28
表4-2 分類矩陣 28
表4-3單一分類樹模型之實驗結果 30
表4-4 分群匹配塑模法驗證 30
表4-5連續過濾法模型驗證 31
表4-6 平衡式詐騙偵測模型驗證 31
表4-7 Total及按年份塑模 32
表4-8 年份模型等值投票實驗結果(60:30) 33
表4-9 年份模型加權投票實驗結果(60:30) 33
表4-10 總評價數區間塑模驗證 34
表4-11 評價區間多模型等值投票實驗結果(60:30) 35
表4-12 附錄B具有0.944準確率的數據組(60:30) 35
表4-13 類別塑模實驗結果 35


圖目錄
圖2-1 連續過濾法(Chang & Chang, 2011) 12
圖2-2 互補式偵測模型(劉祐宏, 2012) 12
圖2-3下載交易歷史紀錄流程圖 14
圖2-4 決策樹範例 15
圖2-5 詐騙模型偵測流程 15
圖3-1 以最適群心塑模之流程圖 18
圖3-2 單一偵測模型 19
圖3-3 多層次詐騙偵測模型 20
圖3-4 時間因子區分為三個不同年份 21
圖3-5 多數決投票的投票流程 24
圖3-6 加權多數投票的投票流程 25
參考文獻 [1] Balingit, R., Trevathan, J., Lee, Y., & Read, W. (2009). A Software Tool for Collecting Data from Online Auctions. Proceedings the 6th International Conference on Information Technology: New Generations, (pp. 922-927).
[2] Chang, J. S., & Chang, W. (2009). An Early Fraud Detection Mechanism for Online Auctions Based on Phased Modeling. Proceeding the 2009 International Workshop on Mobile Systems E-commerce and Agent Technology (MSEAT 2009). December 3-5, Taipei, Taiwan.
[3] Chang, W. and J.-S. (2010a). A Multiple-Phased Modeling Method to Identify Potential Fraudsters in Online Auctions. Proceedings of the 2nd International Conference on Computer Research and Development (ICCRD 2010). May 7-10, Kuala Lumpur, Malaysia.
[4] Chang, W. and Chang, J.-S. (2010b). An Online Auction Fraud Screening Mechanism for Choosing Trading Partners. Proceeding of 2010 the 2nd International Conference on Education Technology and Computer (ICIEE 2010). June 22-24, Shanghai, China.
[5] Chang, W., & Chang, J.-S. (2011). A Novel Two-Stage Phased Modeling Framework for Early Fraud Detection in Online Auctions. Expert System with Applications, vol.38, no.9 , pp. 11244-11260.
[6] Chang, W., & Chang, J.-S. (2012). An Effective Early Fraud Detection Method for Online Auctions. Electronic Commerce Research and Application 11 (2012) 346–360.
[7] Chang, Jau-Shien, and Wong, Hao-Jhen, “Selecting appropriate sellers in online auctions through a multi-attribute reputation calculation method,” Electronic Commerce Research and Applications 10(2): 144-154 (2011)
[8] Chen, Y. L., S. S. Chen, and P. Y. Hsu, “Mining hybrid sequential patterns and sequential rules”, Information Systems, Vol. 27, No. 5, 2004, pp. 345-362.
[9] Chau, D. H., & Faloutsos, C. (2005). Fraud Detection in Electronic Auction. Proceedings of European Web Mining Forum (EWMF 2005) at ECML/PKDD 2005. October 3-7.
[10] Chua, C. E., & Wareham, J. (2004). Fighting Internet Auction Fraud: An Assessment and Proposal. Computer, vol. 37, no. 10 , pp. 31-37.
[11] eBay Inc. (2013). 2013 Quarterly Report. Retrieved Dec 1, 2013, from http://investor.ebay.com/annuals.cfm
[12] eBay Inc. (1995). How Feedback works. Retrieved Aug 11, 2012, from eBay: http://pages.ebay.com/help/feedback/howitworks.html
[13] Gavish, B., & Tucci, C. (2008). Reducing Internet Auction Fraud. Communications of the ACM, vo. 51, no. 5 , pp. 89-97.
[14] Goes, P., Tu, Y., & Tung, A. (2009). Online Auctions Hidden Metrics. Communications of the ACM, 52(4) , pp. 147-149.
[15] Ishioka, T. (2005). An expansion of x-means for automatically determining the optimal number of clusters-progressive iterations of k-means and merging of the clusters. Proceedings of fourth IASTED international conference computational intelleigence. July 4-6, 2005, Calgery, Alberta, Canada.
[16] Josang, A., and Golbeck, J., “Challenges for Robust Trust and Reputation Systems,” in Proceedings of the 5th International Workshop on Security and Trust Management, Saint Malo, France, Sep. 2009.
[17] Josang, A., “Robustness of Trust and Reputation: Does It Matter,” IFIPTM 2012, IFIP AICT 374, pp. 353-262, 2012.
[18] Kaszuba, T., Hupa, Al., and Wierzbicki, A. (2010), “Advanced Feedback Management for Internet Auction Reputation Systems, “ IEEE Internet Computing, Sep/Oct 2010, p. 31-37.
[19] Kauffman, R., & Wood, C. (2003). Running up the Bid: Detecting, Predicting, and Preventing Reserve Price Shilling in Online Auctions. Proceedings of the 5th international conference on Electronic Commerce.
[20] Kobayashi, M., & Ito, T. (2007a). A transactional relationship visualization system in internet auctions. IEEE Computer Society , pp. 248-251.
[21] Kobayashi, M., & Ito, T. (2008). A Transactional Relationship Visualization System in Internet Auctions. Electronic Commerce - Studies in Computational Intelligence, 110(2008) , pp. 87-99..
[22] National White Collar Crime Center (NW3C). (2011, January 1- December 31). 2010 Internet Crime Report. Retrieved Aug 2012, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2010_IC3Report.pdf
[23] National White Collar Crime Center(NW3C). (2012). 2011 Internet Crime Report. Retrieved Aug 2012, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2011_IC3Report.pdf
[24] Nguyen, T. D., and N. R. Jennings (2003), “Concurrent bi-lateral negotiation in agent systems,” Proceedings of the 14th International Workshop on Database and Expert Systems Applications(DEXA’03), pp. 1-6.
[25] Panda, M., & Patra, M. (2009, November). A Novel Classification via Clustering Method for Anomaly Based Network Intrusion Detection System. International Journal of Recent Trends in Engineering, vol.2, no.1 .
[26] Pandit, S., Chau, D., Wang, S., & Faloutsos, C. (2007). Netprobe: a fast and scalable system for fraud detection in online auction networks. WWW '07 Proceedings of the 16th international conference on World Wide Web (pp. 201-210). New York, NY, US: ACM.
[27] Quinlan, J. R. (1993). C4.5 Programs for machine learning. San Mateo, CA: Morgan Kaufmann.
[28] Rubin,S., et al., (2005). An auctioning reputation system based on anomaly detection. Proceedings of the 12th ACM Conference on Computer and Communications Security (CCS).
[29] Sherchan, Wanita, Nepal, Surya, and Paris, C., “A Survey of Trust in Social Networks,” ACM Computing Survey, Vol. 45, No. 4, Article 47. Aug. 2013.
[30] Schmidt, S., Steele, R., Dillon, T., and Chang, E. Fuzzy trust evaluation and credibility development in multi-agent systems. Applied Soft Computing, 7, 2, 2007, 492–505.
[31] Selvaraj, C., and Anand S., “A Survey on Security Issues of reputation Management Systems for Peer-to-Peer Networks,” Computer Science Review 6 (2012) 145-160.
[32] Song, S., Hwang, K., Zhou, R., and Kwok, Y. K. Trusted P2P transactions with fuzzy reputation aggregation. IEEE Internet Computing, Vol. 9, 6, 2005, 24–34.
[33] Tavakolifard, M., and Almeroth, K. C., “Social Comuting: An Intersection of recommender Systems, Trust/Reputation Systems, and Social Network,” IEEE Network, July/August 2012, p. 53-58.
[34] Trevathan, J., & Read, W. (2007). Detecting Collusive Shill Bidding. Proceeding of International Conference on Information Technology (ITNG'07), (pp. 799-808). April 2-4, Las Vegas, Nevada, USA.
[35] Wang, J., & Chiu, C. (2005). Detecting OnlineAuction Inflated-Reputation Behaviors using Social Network Analysis. Proceedings of NAACSOS Conference 2005, June 26-28.
[36] Wierzbicki, A., et al., “Improving Computational trust representation based on Internet Auction traces, “ Decision Support System 54 (2013) 929-940.
[37] Witten, I. H. Data mining: Practical machine learning tools and techniques. Morgan Kaufmann, Burlinton, 3rd ed.2010.
[38] Yu, H., et al., “A Survey of Trust and Repuation Management Systems in Wireless Communications,” Proceedings of the IEEE, Vol. 98, No. 10, Oct. 2010, p.1755-1772.
[39] 洪儀玶,「具早期預警能力之線上拍賣詐騙偵測」,淡江大學資訊管理學系,碩士論文,民96。
[40] 翁豪箴,「考量服務品質之多屬性線上拍賣名聲系統」,淡江大學資訊管理學系,碩士論文,民97。
[41] 梁賀翔,「一套線上拍賣詐騙即時偵測系統」,淡江大學資訊管理學系,碩士論文,民99。
[42] 郎健如,「一套線上拍賣不誠實交易者之二階段偵測方法」,碩士論文,民99。
[43] 葉學杰,「協商中的對手喜好預測與協商策略應用」,碩士論文,民101。
[44] 劉祐宏,「線上拍賣詐騙偵測之屬性挑選與流程設計」,碩士論文,民101。
[45] 莊秉諺,「線上拍賣詐騙行為之時序分析」,淡江大學資訊管理學系,碩士論文,民102。
[46] 曾憲雄、蔡秀滿等人,「資料探勘」,旗標出版,民97。
論文使用權限
  • 同意紙本無償授權給館內讀者為學術之目的重製使用,於2015-07-01公開。
  • 同意授權瀏覽/列印電子全文服務,於2015-07-01起公開。


  • 若您有任何疑問,請與我們聯絡!
    圖書館: 請來電 (02)2621-5656 轉 2281 或 來信