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 U0002-1601201317275500
中文論文名稱 線上拍賣詐騙之有效偵測
英文論文名稱 Effective Fraud Detection in Online Auctions
校院名稱 淡江大學
系所名稱(中) 管理科學學系博士班
系所名稱(英) Doctoral Program, Department of Management Sciences
學年度 101
學期 1
出版年 102
研究生中文姓名 張文熙
研究生英文姓名 Wen-Hsi Chang
學號 893560424
學位類別 博士
語文別 英文
口試日期 2012-12-29
論文頁數 128頁
口試委員 指導教授-張昭憲
委員-李國光
委員-陳永昇
委員-壽大衞
委員-李鴻璋
委員-周清江
中文關鍵字 詐騙偵測  早期預警  線上拍賣  分類法  電子商務 
英文關鍵字 Fraud Detection  Early Warning  Online Auction  Classification  e-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,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。
  為驗證提出方法之有效性,我們搜集台灣雅虎拍賣網站實際交易資料進行實驗。結果顯示,與前人研究相較,本論文提出之方法能有效提升詐騙偵測準確性,並具有早期偵測效果。若搭配所挑選的精簡指標集,也可達到幾近相同的效果。上述結果說明本研究提出方法確實能兼具成本與效用,有助於增進詐騙偵測系統的實用性,提供線上拍賣使用者更安全、更實際的保障。
英文摘要 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.
論文目次 Table of Contents
Chapter 1. Introduction 1
1.1 Motivation and Purpose 3
1.2 Research Scope 8
1.3 Contributions of the Dissertation 9
1.4 Organization of the Dissertation 11
Chapter 2. Literature Review 13
2.1 Reputation Systems used by Auction Houses 13
2.2 Online Auction Fraud Schemes 15
2.3 Early Fraud Detection in Online Auctions 18
2.4 Measured Attributes for Detecting Online Auction Fraud 20
2.5 Modeling Algorithms 23
2.6 Training Data Preparation 28
Chapter 3. Two-Stage Phased Modeling Framework for Early Fraud Detection 30
3.1 Lifespan of a Fraudster 30
3.2 Phase Partitioning Transaction History 33
3.3 Phased Modeling Method 34
3.4 Successive Filtering Procedure 38
3.5 Hybrid-Phased Modeling to Cope with Inconsistent Features of Fraudsters 41
3.6 Two-Stage Fraud Detection Procedure 44
3.7 Experiment of Applying Phased Models 45
3.7.1 Single Phased Models 46
3.7.2 Evaluation Of The Hybrid Phased Detection Model 51
3.7.3 Evaluation of Two-stage Fraud Detection Procedure 53
3.7.4 Phase Prediction 54
Chapter 4. Cost-Effective Methods for Fraud Detection 57
4.1 Candidate Pool of Measured Attributes 57
4.2 Measured Attribute Selection Procedure 61
4.3 Results Applying Single Detection Model 71
4.4 Complement Phased Models 72
4.4.1 The Concept of Late-Profiling 73
4.4.2 Complement phased modeling 76
4.5 Experiment of Applying HCM 81
4.5.1 Data Preparation and Performance Metrics 81
4.5.2 Experimental Results 82
4.6 Discussion on the effect of cost reduction 85
Chapter 5. Fraudulent Behavior Flipping Observation 88
5.1 Fraudulent Behavior Camouflage 88
5.2 Fraudulent Behavior Clustering 90
5.3 Experiment Results of Fraudulent Behavior Clustering 92
5.4 Discussion on Behavior Flipping 97
5.5 Association Rule Mining for Fraudulent Behavior Fluctuation Analysis 100
Chapter 6. Conclusions and Future Work 103
Appendix A- Statistics of Internet Crime Report in 2011 121
Appendix B- Top 10 Internet crime complaint categories 122
Appendix C- Hybrid-phased models vs. individual phased test sets 123
Appendix D- The all 44 measured attribute candidates 124
Appendix E- Behavior difference between the first part and the last part 126
Appendix F- Count of proven fraudsters in Yahoo!Taiwan 127
Publication List 128


List of Tables
Table 1 An example data set profiled by four common attributes 22
Table 2 Transaction history of fraudster momo123 32
Table 3 Results of MC&F (100%) to test T(r%) 48
Table 4 Results of single phased M(r%) to test T(r%) 49
Table 5 Results of applying the successive filtering procedure only 50
Table 6 Results of fraud detection by hybrid-phased models only 51
Table 7 Detecting phase 80% fraudsters with hybrid-phased models only 52
Table 8 Results of two-stage fraud detection procedure 53
Table 9 Performance comparison of phase prediction 55
Table 10 Counts of being detected fraudsters by phased models 56
Table 11 Categories of measured attributes 58
Table 12 Top 5 promising measured attribute in 5 phases 66
Table 13 The 10 selected measured attributes 68
Table 14 The seven selected measured attributes 69
Table 15 Performance comparison of different number of measured attributes 70
Table 16 Results of potential fraudster identification using hybrid phased models 71
Table 17 Distribution of the located phase of break point 83
Table 18 Averaged results of ten runs of hybrid complement phased models 84
Table 19 Performance comparison of the six algorithms 86
Table 20 Results of clustered phased profiles 93
Table 21 Counts of being identified by phased profiles 94
Table 22 Outcome of applying c4.5 decision trees 97
Table 23 Prediction errors 97
Table 24 Fraudulent behavior switching patterns 98
Table 25 The 10 best association rules generated by Apriori 101
List of Figures
Figure 1 An example of classification tree for fraud detection 26
Figure 2 IB1 algorithm 27
Figure 3 Conventional fraud detection methods 31
Figure 4 Phase partitioning based on accumulated ratings 34
Figure 5 Phased r % Model 35
Figure 6 Partition transaction histories for building phased models 37
Figure 7 Using phased models to detect fraudsters 38
Figure 8 Successive filtering procedure 39
Figure 9 Concept of the hybrid-phased models 42
Figure 10 Hybrid-phased models construction 43
Figure 11 Two-stage fraud detection procedure 45
Figure 12 A wrapper by Kohavi & John (1997) 62
Figure 13 The modified wrapper (originated from Kohavi & John, 1997) 64
Figure 14 Attribute selection procedure for early fraud detection in pseudo code 65
Figure 15 Illustration of an identity theft 74
Figure 16 Differences of fraudster profiles and legitimate user profiles 75
Figure 17 Procedure of cost-effective detection 76
Figure 18 Complement phased models 77
Figure 19 Complement phased models inspection 79
Figure 20 Complement phased model construction in pseudo code 80
Figure 21 Different cluster presentations 92
Figure 22 General types of fraudulent behavior 95
Figure 23 Examples of fraudulent behavior switching 98

參考文獻 References
[1] Aha, D., & Kibler, D. (1991). Instance-based learning algorithms. Machine Learning, 6, pp. 37-66.
[2] Balingit, R., Trevathan, J., Lee, Y., & Read, W. (2009). A software tool for collecting data from online auctions. Proceedings of the 6th International Conference on Information Technology: New Generations (pp. 922-927). Las Vegas, Nevada: IEEE Computer Society.
[3] Berry, B., Erdogan, G., & Trigueiros, D. (1995). Rule induction for financial modeling and model interpretation. Proceedings of the 28th Hawaii International Conference on System Sciences, (pp. 177-186). Hawaii, USA.
[4] Bi, J., Bennett, K., Embrechts, M., Breneman, C., & Song, M. (2003, March 1). Dimensionality reduction via sparse support vector machines. The Journal of Machine Learning Research, 3, pp. 1229-1243.
[5] Blum, A., & Langley, P. (1997). Selection of relevant feature and examples in machine learning. Artificial Intelligence, 97(1-2), pp. 245-271.
[6] Bontempi, G. (2005). Structural feature selection for wrapper methods. Proceedings of European Symposium on Artificial Neural Networks (pp. 405-410). Bruges, Belgium: d-side publi.
[7] Buchegger, S., & Boudec, J. (2003). A robust reputation system for mobile ad-hoc networks. EPFL IC Technical Report, pp. 1-11.
[8] Burge, P., Shawe-Taylor, J., Cooke, C., Moreau, Y., Preneel, B., & Stoermann, C. (1997). Fraud detection and management in mobile telecommunications networks. Proceedings of European Conference on Security and Detection, (pp. 91-96). London, UK.
[9] Chan, P., Fan, W., Prodromidis, A., & Stolfo, S. (1999). Distributed data mining in credit card fraud detection. IEEE Intelligent Systems, 14(6), pp. 67-74.
[10] Chandola, V., Banerjee, A., & Kumar, V. (2009, July). Anomaly detection: A survey. ACM Computing Survery, 41(3), pp. 11-58.
[11] Chang, J., & Chang, W. (2009). An Early Fraud Detection Mechanism for Online Auctions Based on Phased Modeling. Proceedings of the 2009 International Workshop on Mobile Systems E-commerce and Agent Technology, (pp. 743-748). Taipei, Taiwan.
[12] Chang, J., & Chang, W. (2012). A Cost-Effective Method for Early Fraud Detection in Online Auctions. Proceedings of 2012 10th International Conference on ICT and Knowledge Engineering, (pp. 182-188). Bangkok, Thailand.
[13] Chang, W., & Chang, J. (2010). A Multiple-Phased Modeling Method to Identify Potential Fraudsters in Online Auctions. Proceedings of the 2nd International Conference on Computer Research and Development, (pp. 186-190). Kuala Lumpur, Malaysia.
[14] Chang, W., & Chang, J. (2010). An Online Auction Fraud Screening Mechanism for Choosing Trading Partners. Proceedings of 2010 the 2nd International Conference on Education Technology and Computer, 5, pp. V5-56 - V5-60. Shanghai, China.
[15] Chang, W., & Chang, J. (2010). Using Clustering Techniques to Analyze Fraudulent Behavior Changes in Online Auctions. Proceedings of 2010 International Conference on Networking and Information Technology, (pp. 34-38). Manila, Philippine.
[16] Chau, D. H., & Faloutsos, C. (2005). Fraud detection in electronic auction. Proceedings of European Web Mining Forum at ECML/PKDD. Porto, Portugal.
[17] Chau, D. H., & Faloutsos, C. (2005). Fraud detection in electronic auction. Proceedings of European Web Mining Forum. Porto, Portugal.
[18] Chau, D. H., Pandit, S., & Faloutsos, C. (2006). Detecting fraudulent personalities in networks of online auctioneers. Proceedings of the 10th European Conference on Principle and Practice of Knowledge Discovery in Databases (pp. 103-114). Berlin, Germany: Springer-Verlag Berlin, Heidelberg.
[19] Chau, P. (2011, March). Catching bad guys with graph mining, XRDS: Crossroads. The ACM Magazine for Students, 17(3), pp. 16-18.
[20] Chawla, N., Bowyer, K., Hall, L., & Kegelmeyer, W. (2001, January). SMOTE: synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16(1), pp. 321-357.
[21] Chua, C. E., & Wareham, J. (2004, October). Fighting Internet auction fraud: an assessment and proposal. Computer, 37(1), pp. 31-37.
[22] Curry, S. (2001, March 29). Online auctions: the bizarre bazaar. Internet Scambuster, 43(1), pp. 1-6.
[23] Das, S. (2001). Filters, wrappers and a boosting-based hybrid for feature selection. Proceedings of the Eighteenth International Conference on Machine Learning (pp. 74-81). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
[24] Dash, M., & Liu, H. (1997). Feature selection for classification. Intelligent Data Analysis, 1, pp. 131-151.
[25] Dohono, S. (2004). Early detection of insider trading in option markets. Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 420-429). Seattle, WA, USA: ACM New York, NY, USA.
[26] eBay Inc. (2011). 2011 Annual Report / Form 10-K. Retrieved June 1, 2012, from http://files.shareholder.com/downloads/ebay/1903545909x0xS1065088-12-6/1065088/filing.pdf
[27] Estabrooks, A., Jo, T., & Japkowicz, N. (2004, February). A Multiple Resampling Method for Learning from Imbalanced Data Sets. Computational Intelligence, 20(1), pp. 18-36.
[28] Fake Feedback Scam Affects eBay Sellers and Buyers Alike. (2006, June 18). Retrieved March 1, 2011, from Internet Journal-Internet Marketing Blog: http://www.theinternetone.net/Fake_Feedback_Scam_Affects_eBay_Sellers_and_Buyers_Alike_930.html
[29] Frank, E., & Witten, I. H. (1998). Generating accurate rule sets without global optimization. In J. W. Shavlik (Ed.), Proceedings of the 15th International Conference on Machine Learning (pp. 144-151). San Francisco, CA: Morgan Kaufmann Publishers.
[30] Gavish, B., & Tucci, C. (2008). Reducing Internet auction fraud. Communications of the ACM, 51(5), pp. 89-97.
[31] Ghosh, S., & Reilly, L. D. (1994). Credit card fraud detection with a neural network. Proceedings of the Twentieth-seventh Annual Hawaii International Conference on System Sciences, 3, pp. 621-630.
[32] Gilad-Bachrach, R., Navot, A., & Tishby, N. (2004). Margin based feature selection - theory and algorithms. Proceedings of the 21st International Conference on Machine Learning (pp. 43-69). Banff, Alberta, Canada: ACM, New York, NY.
[33] Giraud-Carrier, C., & Povel, O. (2003, August). Characterising data mining software. Intelligent Data Analysis, 7(3), pp. 181-192.
[34] Goes, P., Tu, Y., & Tung, A. (2009). Online auctions hidden metrics. Communications of the ACM, 52(4), pp. 147-149.
[35] Goodrich, M., & Kerschbaum, F. (2011). Privacy-enhanced reputation-feedback methods to reduce feedback extortion in online auctions. Proceedings of ACM Conference on Data and Application Security and Privacy.
[36] Gregg, D., & Scott, J. (2008, April). A typology of complaints about eBay sellers. Communications of the ACM, 51(4), pp. 69-74.
[37] Guyon, I., & Elisseeff, A. (2003, March 1). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, pp. 1157-1182.
[38] Hall, M. A., & Smith, A. (1999). Feature selection for machine learning: comparing a correlation-based filter approach to the wrapper. Proceedings of the Twelfth International Florida Artificial Intelligence Research Society Conference (pp. 235-239). AAAI Press.
[39] Huang, C., & Wang, C. (2006). A GA-based feature selection and parameters optimization for support vector machines. Expert Systems with Applications, 31, pp. 231-240.
[40] Huynh, D., Jennings, N., & Shadbolt, N. (2004). Developing an integrated tust and reputation model for open multi-agent systems. Proceedings of the 7th International Workshop on Trust in Agent Societies-Autonomous Agents & Multi-Agent Systems Conference, (pp. 65-74). New York, USA.
[41] Huynh, D., Jennings, N., & Shadbolt, N. (2004). FIRE: An integrated trust and reputation model for open multi-agent systems. Proceedings of the 16th European Conference on Artificial Intelligence, (pp. 18-22). Valencia, Spain.
[42] Huynh, D., Jennings, N., & Shadbolt, N. (2006, March). An integrated trust and reputation model for open multi-agent systems. Autonomous Agents and Multi-Agent Systems, 3(2), pp. 119-154.
[43] Institute for Information Industry (III), Taiwan. (2011). 2010 online market scale reaches 358.3 billion. Retrieved January 28, 2011, from http://mic.iii.org.tw/intelligence/pressroom/pop_pressfull.asp?sno=225&type1=2
[44] John, G., Kohavi, R., & Pfleger, P. (1994). Irrelevant features and the subset selection problem. machine learning. Proceedings of the Eleventh International Conference (pp. 121-129). Morgan Kaufmann.
[45] Kauffman, R. J., & Wood, C. A. (2007). Irregular bidding from opportunism: an exploration of shilling in online auctions. Information Systems Research, VV, pp. 1-36.
[46] Kira, K., & Rendell, L. (1992). A practical approach to feature selection. Proceeding of the 9th International Workshop on Machine Learning (pp. 249-256). San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
[47] Kirkos, E., Spathis, C., & Manolopoulos, Y. (2007, May). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), pp. 995-1003.
[48] Kobayahi, M., & Ito, T. (2007). A transactional relationship visualization system in internet auctions. Proceedings of the 2007 IEEE/WIC/ACM International Conference on Intelligent Agent Technology (pp. 248-251). Washington, DC, USA: IEEE Computer Society.
[49] Kohavi, R., & John, H. (1997, December). Wrappers for feature subset selection. Artificial Intelligence, 97(1-2), pp. 273-324.
[50] Kononenko, I. (1994). Estimating attributes: analysis and extensions of RELIEF. Proceeding of European Conference on Machine Learning (pp. 171-182). Secaucus, NJ, USA: Springer-Verlag Inc.
[51] Ku, Y., Chen, Y., & Chiu, C. (2007). A proposed data mining approach for Internet auction fraud detection. Proceedings of the 2007 Pacific Asia Conference on Intelligence and Security Informatics, 4403, pp. 238-243.
[52] Kubat, M., & Matwin, S. (1997). Addressing the Curse of Imbalanced Training Sets: One-Sided Selection. Proceedings of the Fourteenth International Conference on Machine Learning (pp. 179-186). Morgan Kaufmann.
[53] Kubat, M., Holte, R., & Matwin, S. (1997). Learning when negative examples abound. Proceedings of 9th European Conference on Machine Learning, (pp. 146-153). Prague, Czech Republic.
[54] Law, M., Jain, A., & Figueiredo, A. (2004, September). Simultaneous feature selection and clustering using mixture models. IEEE Transactions on Pattern Analysis and Machine Intelligence, 26(9), pp. 1-13.
[55] Ling, C., & Li, C. (1998). Data mining for direct marketing: problems and solutions. Proceedings of Knowledge Discovery and Data Mining, (pp. 73-79).
[56] Liu, H., & Setiono, R. (1997, July). Feature selection via discretization. IEEE Trans. on Knowledge and Data Engineering, 9(4), pp. 642-645.
[57] Liu, H., & Yu, L. (2005, April). Toward integrating feature selection algorithms for classification and clustering. IEEE Transactions on Knowledge and Data Engineering, 17(4), pp. 491-502.
[58] Liu, L., & Fern, X. (2012). Constructing training sets for outlier detection. Proceedings of the Twelfth SIAM International Conference on Data Mining (pp. 919-929). Anaheim, California, USA: SIAM / Omnipress.
[59] Liu, X. J., & Zhou, Z. (2006). Exploratory Under-Sampling for Class-Imbalance Learning. Proceedings of the Sixth International Conference on Data Mining (pp. 965-969 ). IEEE Computer Society Washington, DC, USA.
[60] Mao, K. (2004). Feature subset selection for support vector machines through discriminative funciton pruning analysis. IEEE Transactions on Systems, Man, and Cybernetics, 34(1), pp. 60-67.
[61] Maranzato, R., Pereira, A., Lago, A., & Neubert, M. (2010). Fraud detection in reputation systems in e-markets using logistic regression. Proceedings of the 2010 ACM Symposium on Applied Computing, March, (pp. 14-26).
[62] Maranzato, R., Pereira, A., Neubert, M., & Lago, A. (2010, June). Fraud detection in reputation systems in e-markets using logistic regression and stepwise optimization. SIGAPP Applied Computing Review, 11(1), pp. 14-26.
[63] Marti, S., & Molina, H. (2006, March). Taxonomy of trust: categorizing P2P reputation systems. Computer Networks, 50(4), pp. 472-484.
[64] McGlohon, M., Bay, S., Anderle, M., Steier, D., & Faloutsos, C. (2009). SNARE: A link analytic system for graph labeling and risk detection. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (pp. 1265-1274). ACM New York, NY, USA.
[65] Michelle, T. M. (1997). Machine Learning (International ed.). Singapore: McGrow-Hill.
[66] National White Collar Crime Center (NW3C). (2008). 2007 Internet Crime Report. Retrieved Aug 31, 2012, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2007_IC3Report.pdf
[67] National White Collar Crime Center (NW3C). (2009). 2008 Internet Crime Report. Retrieved Aug 12, 2012, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2008_IC3Report.pdf
[68] National White Collar Crime Center (NW3C). (2010). 2009 Internet Crime Report. Retrieved March 1st, 2011, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2009_IC3Report.pdf
[69] National White Collar Crime Center (NW3C). (2011). 2010 Internet Crime Report. Retrieved May 27, 2012, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2010_IC3Report.pdf
[70] National White Collar Crime Center (NW3C). (2012). 2011 Internet Crime Report. Retrieved May 27, 2012, from Internet Crime Complaint Center: http://www.ic3.gov/media/annualreport/2011_IC3Report.pdf
[71] Pandit, S., Chau, D. H., Wang, S., & Faloutsos, C. (2007). NetProbe: A fast and scalable system for fraud detection in online auction networks. Proceedings of the 16th International Conference on World Wide Web, (pp. 201-210).
[72] Pavlou, P., & Dimoka, A. (2006, December). The nature and role of feedback text comments in online marketplaces: Implications for trust building, price premiums, and seller differentiation. Information Systems Research, 17(4), pp. 392-414.
[73] Pelleg, D., & Moore, A. (2000). X-means: Extending K-means with efficient estimation of the number of clusters. Proceedings of the Seventeenth international Conference on Machine Learning (pp. 727-734). Morgan Kaufmann Publishers Inc. San Francisco, CA, USA.
[74] Quinlan, J. R. (1993). C4.5 Programs for Machine Learning. San Mateo, CA: Morgan Kaufmann.
[75] Resnick, P., Zeckhauser, R., Friedman, E., & Kuwabara, K. (2000, December). Reputation systems. Communications of the ACM, 43(12), pp. 45-48.
[76] Robnik-Sikonja, M., & Kononenko, I. (1997). An adaptation of relief for attribute estimation in regression. Proceeding of the 14th International Conference on Machine Learning, (pp. 296-304).
[77] Rubin, S., Christodorescu, M., Ganapathy, V., Giffin, J., Kruger, L., Wang, H., & Kidd, N. (2005). An auctioning reputation system based on anomaly. Proceedings of the 12th ACM Conference on Computer and Communications Security (pp. 270-279). Alexandria, VA, USA: ACM New York, NY, USA.
[78] Saeys, Y., Inza, I., & Larrañaga, P. (2007, September). A review of feature selection techniques in bioinformatics. Bioinformatics, 23(19), pp. 2507-2517.
[79] Sebban, M., & Nock, R. (2002). A hybrid filter/wrapper approach of feature selection using information theory. Pattern Recognition, 35, pp. 835–846.
[80] Shao, H.; Zhao, H.; Chang, G. (2002). Applying data mining to detect fraud behavior in customs declaration. Proceedings of the First International Conference on Machine Learning andCybernetics, 3, pp. 1241-1244.
[81] Shen, Z., & Sundaresan, N. (2011). eBay: an E-commerce marketplace as a complex network. Proceedings of the Fourth ACM International Conference on Web Search and Data Mining (pp. 655-664). ACM New York, NY, USA.
[82] Sikora, R., & Piramuthu, S. (2007, July 16). Framework for efficient feature selection in genetic algorithm based data mining. European Journal of Operational Research, 180(2), pp. 723–737.
[83] Wang, J., & Chiu, C. (2005). Detecting online auction inflated-reputation behaviors using social network analysis. Proceedings of North American Association for Computational Social and Organizational ScienceConference 2005. Notre Dame, Indiana, USA.
[84] Wang, J., & Chiu, C. (2008). Recommending trusted online auction sellers using social network analysis. Expert Systems with Applications, 34(3), pp. 1666-1679.
[85] Witten, I. H., & Frank, E. (2005). Data mining: Practical Machine Learning Tools and Techniques (2nd ed.). San Francisco: Morgan Kaufmann.
[86] Witten, I. H., Frank, E., & Hall, M. (2010). Data Mining: Practical Machine Learning Tools and Techniques (3rd ed.). Burlinton: Morgan Kaufmann.
[87] Wu, X., Kumar, V., Quinlan, J., Ghosh, J., Yang, Q., Motoda, H., . . . Steinberg, D. (2007). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), pp. 1-37.
[88] Yang, C., Chuang, L., & Yang, C. (2010). IG-GA: A hybrid filter/wrapper method for feature selection of microarray data. Journal of Medical and Biological Engineering, 30(1), pp. 23-28.
[89] Yolum, P., & Singh, M. P. (2003). Emergent properties of referral systems. Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent Systems, (pp. 592-599).
[90] Yu, B., & Singh, M. P. (2003). Detecting deception in reputation management. Proceedings of the Second International Joint Conference on Autonomous Agents and Multiagent systems, (pp. 73-80).
[91] Yu, E., & Cho, S. (2006, September). Ensemble based on GA wrapper feature selection. Computers and Industrial Engineering - Special issue: Computational Intelligence and Information Technology Applications to Industrial Engineering Selected Papers from the 33rd ICC&IE, 51(1), pp. 111-116.
[92] Yu, L., & Liu, H. (2004, December 1). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, pp. 1205-1224.
[93] Zhang, B., Zhou, Y., & Faloutsos, C. (2008). Toward a comprehensive model in Internet auction fraud detection. Proceedings of the 41st Annual Hawaii International Conference on System Sciences : January 7-10, (pp. 1-9). Waikoloa, HI, USA.
[94] Zhong, N., & Dong, J. (2001). Using rough sets with heuristics for feature selection. Journal of Intelligent Information Systems, 16, pp. 199-214.
[95] Zhuo, L., Zheng, J., Wang, F., Li, X., Ai, B., & Qian, J. (2008). A GEA genetic algorithm based wrapper feature selection method for classification of hyperspectral images using support machine. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XXXVII(Part B7), pp. 397-402.

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2013-01-24公開。
  • 同意授權瀏覽/列印電子全文服務,於2013-01-24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