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。

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.

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

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