Fraud Detection for Internet Auctions: A Data Mining Approach

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研究生：	尹麗莎 Karen Elisa Ochaeta Paz
論文名稱：	Fraud Detection for Internet Auctions: A Data Mining Approach 以資料探勘技術進行網路拍賣之詐欺偵測
指導教授：	魏志平 Chih-Ping Wei
口試委員:
學位類別：	碩士 Master
系所名稱：	科技管理學院 - 科技管理研究所 Institute of Technology Management
論文出版年：	2008
畢業學年度：	96
語文別：	英文
論文頁數：	54
中文關鍵詞：	網路拍賣、網路拍賣詐騙、詐騙偵測、數據採樣、樹狀決策歸納、演算法
外文關鍵詞：	Internet Auction, Internet Auction Fraud, Fraud Detection, Data Mining, Decision Tree Induction, Support Vector Machines
相關次數：	點閱：3 下載：0
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Internet auctions are a clear example of how information and networking technologies have transformed traditional trading processes limited to specific events and products, into pervasive and thriving electronic business models enabled by the Internet; they have increased the variety of goods and services that can be traded using auction mechanisms and reduced transaction costs for both buyers and sellers. Nevertheless, due to the characteristics of electronic transactions, it is difficult to distinguish genuine sources of information from fraudulent ones, increasing the vulnerability of participants to be victims of fraudsters. Since Internet auction fraud has become the most frequently reported form of Internet scams, auction websites are trying to provide protection to their customers implementing new services that threaten the low cost structure of this business model and may as well increase the complexity of transactions over time. To counteract the effects of fraud and the threat that it poses to the future of auction websites, specifically the consumer-to-consumer type, this study provides a data mining approach to fraud detection based on variables derived from auctioneers’ transaction history. Using the historical data recorded on Yahoo! Auction site for the transactions of both fraudsters and legitimate auctioneers, our proposed fraud detection model outperforms similar methods aimed at identifying potential fraudsters among participants of Internet auctions.

ACKNOWLEDGEMENT..........................................................................................................III
LIST OF TABLES....................................................................................................................VII
LIST OF FIGURES.................................................................................................................VIII
CHAPTER 1 INTRODUCTION.................................................................................................1
1.1 BACKGROUND..................................................................................................................1
1.2 MOTIVATION....................................................................................................................2
1.3 OBJECTIVE......................................................................................................................3
CHAPTER 2 LITERATURE REVIEW....................................................................................4
2.1 INTERNET AUCTIONS........................................................................................................4
2.2 TYPES OF INTERNET AUCTIONS........................................................................................6
2.3 INTERNET AUCTION FRAUD...........................................................................................10
2.3.1 Types of Fraud on Internet Auctions.....................................................................13
2.4 EXISTING MECHANISMS FOR FRAUD DETECTION ON INTERNET AUCTIONS....................15
2.4.1 Auction Communities.............................................................................................15
2.4.2 Feedback-based Reputation Systems.....................................................................16
2.4.3 Data Mining Approach..........................................................................................18
2.4.4 Social Network Analysis........................................................................................22
CHAPTER 3 PROPOSED FRAUD DETECTION MODEL.................................................24
3.1 PROBLEM DESCRIPTION.................................................................................................24
3.2 VARIABLES....................................................................................................................24
3.2.1 Variables Based on Price of Items Sold.................................................................25
3.2.2 Variables Based on Transaction Frequency..........................................................26
3.2.3 Variables Related to Transaction Profile..............................................................27
3.2.4 Variables Based on Reputation/Feedback Ratings................................................28
3.3 CLASSIFICATION TECHNIQUES: C4.5 AND SUPPORT VECTOR MACHINES (SVM)...........30
CHAPTER 4 DATA COLLECTION AND EVALUATION DESIGN.................................33
4.1 DATA COLLECTION........................................................................................................33
v
4.2 EVALUATION DESIGN.....................................................................................................34
4.3 EVALUATION PROCEDURE AND CRITERIA......................................................................37
CHAPTER 5 EVALUATION RESULTS.................................................................................39
5.1 COMPARATIVE EVALUATION OF C4.5 AND SVM OF THE PROPOSED FRAUD DETECTION MODEL................................................................................................................................39
5.2 COMPARATIVE EVALUATION OF BENCHMARKS AND THE PROPOSED MODEL................42
CHAPTER 6 CONCLUSIONS AND FUTURE RESEARCH...............................................46
REFERENCES...........................................................................................................................48
APPENDIX 51
A.1 DATA GENERATED BY C4.5 AND SVM ALGORITHMS (USING WEKA VERSION 3.4.12) FOR THE PROPOSED FRAUD DETECTION MODELS.......................................................................51
A.2 AVERAGE RANKING OF ALL VARIABLES USED IN THE PROPOSED FRAUD DETECTION MODEL................................................................................................................................53

                                

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