Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches

Chiu, Chaochang; Ku, Yungchang; Lie, Ting; Chen, Yuchi

doi:10.2753/jec1086-4415150306

Cited by 40 publications

(45 citation statements)

References 27 publications

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“…Accordingly, many approaches have tried to detect fraudsters by detecting the cohesive groups in the social network built from the transaction history. The notion of k-core has been found to be more feasible than other notions for cohesive groups (e.g., component, clique and k-plex) to detect fraudsters [2,13]. A common way to build the social network is to represent each trader as a node, and each transaction between two traders as an edge connecting the two corresponding nodes of the two traders.…”

Section: Related Workmentioning

confidence: 99%

“…For center weight, they checked whether center weight > 0 holds. Chiu et al [13] used social network metrics such as k-core, k-plex, n-clique, normalized betweenness and degree to build a decision tree for fraud detection. They also suggested that abnormal accounts can be identified in the k-cores with k ≥ 2.…”

Section: Related Workmentioning

confidence: 99%

“…In the literature, k-core was found useful to detect cohesive groups of fraudsters and their accomplices [2,6,13]. According to [6,13], fraudsters are more likely to reside in the 2-core subgroups.…”

Section: Neighbor Diversity On K-corementioning

confidence: 99%

“…According to [6,13], fraudsters are more likely to reside in the 2-core subgroups. Thus, the k-core is the third chosen attribute for calculating neighbor diversity in this study.…”

Section: Neighbor Diversity On K-corementioning

confidence: 99%

“…In contrast, most recent approaches have adopted a social network approach. They build a graph based on accounts' transaction history, and then apply Social Network Analysis (SNA) to detect cohesive groups in the graph since the accounts in a collusive group often have extensive transactions within the group to boost their reputations [2,3,[5][6][7][8][9][10][11][12][13]. To detect the cohesive groups in the graph, the notion of k-core has been found to be more feasible than other notions for cohesive groups, such as component, clique and k-plex [2,13].…”

Section: Introductionmentioning

confidence: 99%

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Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Lin

Khomnotai

2014

Entropy

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Online auctions attract not only legitimate businesses trying to sell their products but also fraudsters wishing to commit fraudulent transactions. Consequently, fraudster detection is crucial to ensure the continued success of online auctions. This paper proposes an approach to detect fraudsters based on the concept of neighbor diversity. The neighbor diversity of an auction account quantifies the diversity of all traders that have transactions with this account. Based on four different features of each trader (i.e., the number of received ratings, the number of cancelled transactions, k-core, and the joined date), four measurements of neighbor diversity are proposed to discern fraudsters from legitimate traders. An experiment is conducted using data gathered from a real world auction website. The results show that, although the use of neighbor diversity on k-core or on the joined date shows little or no improvement in detecting fraudsters, both the neighbor diversity on the number of received ratings and the neighbor diversity on the number of cancelled transactions improve classification accuracy, compared to the state-of-the-art methods that use k-core and center weight.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Neighbor Diversity On K-corementioning

confidence: 99%

“…According to [6,13], fraudsters are more likely to reside in the 2-core subgroups. Thus, the k-core is the third chosen attribute for calculating neighbor diversity in this study.…”

Section: Neighbor Diversity On K-corementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Lin

Khomnotai

2014

Entropy

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A review of data mining applications in crime

Hassani

Huang

Silva

et al. 2016

Statistical Analysis

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Crime continues to remain a severe threat to all communities and nations across the globe alongside the sophistication in technology and processes that are being exploited to enable highly complex criminal activities. Data mining, the process of uncovering hidden information from Big Data, is now an important tool for investigating, curbing and preventing crime and is exploited by both private and government institutions around the world. The primary aim of this paper is to provide a concise review of the data mining applications in crime. To this end, the paper reviews over 100 applications of data mining in crime, covering a substantial quantity of research to date, presented in chronological order with an overview table of many important data mining applications in the crime domain as a reference directory. The data mining techniques themselves are briefly introduced to the reader and these include entity extraction, clustering, association rule mining, decision trees, support vector machines, naive Bayes rule, neural networks and social network analysis amongst others

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Business and Consumer Economic Wellbeing

Xiao

2015

Consumer Economic Wellbeing

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In the marketplace, consumers and businesses are two major players. Consumers play dual roles to contribute to the economy, most of whom working for businesses to earn incomes and purchasing products from businesses. A perfect market works when participating players follow a set of rules that ensure effi cient and fair operation of the economy. In reality, the market is imperfect and both good and bad business behaviors occur. Good business behavior is defi ned as business practices that are not only consider their own interests but also consumer interests or interests of other parties directly or indirectly involved in the transactions. Bad business behaviors are defi ned as business practices that hurt consumer wellbeing and public interests such as marketing frauds and rip offs. This chapter reviews several concepts that lead to good business behaviors such as corporate social responsibility and quality of life marketing, etc. It also examines the research literature relevant to marketing frauds, rip offs, and consumer victims.

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Internet Auction Fraud Detection Using Social Network Analysis and Classification Tree Approaches

Cited by 40 publications

References 27 publications

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

Using Neighbor Diversity to Detect Fraudsters in Online Auctions

A review of data mining applications in crime

Business and Consumer Economic Wellbeing

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