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研究生:許妙雲
研究生(外文):Laksamee Khomnotai
論文名稱:線上拍賣之詐欺偵測
論文名稱(外文):FRAUDSTER DETECTION IN ONLINE AUCTION
指導教授:林志麟林志麟引用關係
指導教授(外文):Jun-Lin Lin
口試委員:王彥文劉鎮豪楊錦生許嘉裕
口試委員(外文):Yan-Wen WangChen-Hao LiuChin-Sheng YangChia-Yu Hsu
口試日期:2015-06-11
學位類別:博士
校院名稱:元智大學
系所名稱:資訊管理學系
學門:電算機學門
學類:電算機一般學類
論文種類:學術論文
畢業學年度:103
語文別:英文
論文頁數:127
中文關鍵詞:online auction; fraudster detection; diversity; entropy; neighbor diversity; neighbor feature; neighbor-driven attributeonline auctionfraudster detectiondiversityentropyneighbor diversityneighbor featureneighbor-driven attribute
外文關鍵詞:online auction; fraudster detection; diversity; entropy; neighbor diversity; neighbor feature; neighbor-driven attributeonline auctionfraudster detectiondiversityentropyneighbor diversityneighbor featureneighbor-driven attribute
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Online auction has been proposed as a trading platform on internet for more than a decade. It attracts not only legitimate users who attempt to sell their products but also fraudulent users, who desire to commit false transactions for deceiving the third party. It is difficult to separate fraudsters from legitimate users only relying on reputation scores of the traders. Accordingly, fraudster detection is important to ensure the continued success of online auctions. The purpose of this dissertation is to increase the classification performance for detecting fraudsters with inflated reputation. We propose a novel framework for fraudster detection based on concept of neighbor features. The neighbor features of an auction account are calculated from the feature of all traders that have transactions with this account.
In order to distinguish fraudsters from non-fraudsters, we propose a framework that differentiates fraudsters from non-fraudsters based on various features of each trader (i.e., various types of neighbor diversity and various neighbor-driven attributes). The proposed framework consists of three parts, i.e., neighbor diversity based on Shannon entropy, various forms of neighbor diversity, and neighbor-driven attribute. In this dissertation, various forms of neighbor features have been calculated using different approaches. Several features of neighbor have been used for detecting fraudsters from online auction transactions. Real world online auction dataset crawled from Ruten has been used for conducting the experiments in this dissertation. The results indicate that neighbor features enhance overall classification performance, compared to the state-of-the-art methods that use k-core and center weight.

Online auction has been proposed as a trading platform on internet for more than a decade. It attracts not only legitimate users who attempt to sell their products but also fraudulent users, who desire to commit false transactions for deceiving the third party. It is difficult to separate fraudsters from legitimate users only relying on reputation scores of the traders. Accordingly, fraudster detection is important to ensure the continued success of online auctions. The purpose of this dissertation is to increase the classification performance for detecting fraudsters with inflated reputation. We propose a novel framework for fraudster detection based on concept of neighbor features. The neighbor features of an auction account are calculated from the feature of all traders that have transactions with this account.
In order to distinguish fraudsters from non-fraudsters, we propose a framework that differentiates fraudsters from non-fraudsters based on various features of each trader (i.e., various types of neighbor diversity and various neighbor-driven attributes). The proposed framework consists of three parts, i.e., neighbor diversity based on Shannon entropy, various forms of neighbor diversity, and neighbor-driven attribute. In this dissertation, various forms of neighbor features have been calculated using different approaches. Several features of neighbor have been used for detecting fraudsters from online auction transactions. Real world online auction dataset crawled from Ruten has been used for conducting the experiments in this dissertation. The results indicate that neighbor features enhance overall classification performance, compared to the state-of-the-art methods that use k-core and center weight.

TITLE PAGE i
APPROVAL ii
AUTHORIZATION iii
ABSTRACT vi
ACKNOWLEDGEMENT viii
Table of Contents xi
List of Tables xvii
List of Figures xxii
Explanation of Symbols and Nomenclature xxiii
Chapter 1 Introduction 1
1.1 Research Background 1
1.2 Research Objectives 4
1.3 Organization of the Dissertation 5
Chapter 2 Literature Review 7
2.1 Reputation System in Online Auction 7
2.2 Fraud Detection System 8
2.3 Social Network Analysis 11
2.4 Data Mining and Application 15
2.4.1 Cross-Validation 16
2.4.2 Classification Tree J48 17
2.4.3 Artificial Neural Networks 17
2.4.4 Support Vector Machine 18
2.4.5 Weka 18
2.5 Evaluating Classification Performance 19
Chapter 3 Research Framework 23
3.1 Neighbor Diversity 25
3.2 Variations of Neighbor Diversity 28
3.3 Neighbor-Driven Attribute 30
Chapter 4 Data Collection and Network Construction 33
4.1 Collecting the List of Auction Accounts 33
4.2 Construction of Social Network 37
Chapter 5 Neighbor Diversity for Fraudsters in Online Auctions 40
5.1 Neighbor Diversity 41
5.1.1 Neighbor Diversity on the Number of Received Ratings 43
5.1.2 Neighbor Diversity on the Number of Cancelled Transactions 44
5.1.3 Neighbor Diversity on k-core 44
5.1.4 Neighbor Diversity on the Joined Date 45
5.2 Experimental Results 46
5.2.1 Setting 46
5.2.2 Results on Comparing the Mean Neighbor Diversity between Groups 48
5.2.3 Results on Classification Performance 49
5.2.4 Discussion 54
5.3 Summary 56
Chapter 6 Variations of Neighbor Diversity for Fraudster Detection in Online Auction 58
6.1 Variants of Neighbor Diversity 60
6.1.1 Shannon Entropy Diversity 61
6.1.2 Canonical Form of Diversity 61
6.2 Experimental Settings 63
6.3 Experimental Results 64
6.3.1 Results on the Diversity 65
6.3.2 Results on the Addition of k-core in combination with Center Weight 69
6.3.3 Discussion 73
6.4 Summary 75
Chapter 7 Neighbor-Driven Attribute 76
7.1 Problem with Neighbor Diversity 78
7.2 Neighbor-Driven Attribute 79
7.2.1 Maxima Feature 80
7.2.2 Minima Feature 81
7.2.3 Average Feature 81
7.2.4 Standard Deviation Feature 82
7.3 Experimental Settings 82
7.4 Experimental Results 84
7.4.1 Results on the Entire Dataset 86
7.4.2 Results on the Dataset of the Accounts with only One-Class Neighbor 89
7.4.3 Results on the Dataset of the Accounts with More than One-Class Neighbor 92
7.4.4 Discussion 95
7.5 Summary 98
Chapter 8 Conclusion and Future Study 101
8.1 Discussion 104
8.2 Conclusion and Future Study 106
References 108
Curriculum Vitae (CV) 120
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