Detection of Auction Fraud in Commercial Sites
Abstract: Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Fu...
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Universidad de Talca
2020
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oai:scielo:S0718-187620200001001072019-12-17Detection of Auction Fraud in Commercial SitesAnowar,FarzanaSadaoui,Samira Auction fraud Fraud detection Shill bidding Data clustering Data labeling Data sampling supervised classification Abstract: Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. For our study, we employ an actual training dataset, but the data are unlabeled. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach. Since shill bidding datasets are imbalanced, we assess advanced over-sampling, under-sampling and hybrid-sampling methods and compare their performances based on several classification algorithms. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities.info:eu-repo/semantics/openAccessUniversidad de TalcaJournal of theoretical and applied electronic commerce research v.15 n.1 20202020-01-01text/htmlhttp://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762020000100107en10.4067/S0718-18762020000100107 |
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English |
topic |
Auction fraud Fraud detection Shill bidding Data clustering Data labeling Data sampling supervised classification |
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Auction fraud Fraud detection Shill bidding Data clustering Data labeling Data sampling supervised classification Anowar,Farzana Sadaoui,Samira Detection of Auction Fraud in Commercial Sites |
description |
Abstract: Online auctions have become one of the most convenient ways to commit fraud due to a large amount of money being traded every day. Shill bidding is the predominant form of auction fraud, and it is also the most difficult to detect because it so closely resembles normal bidding behavior. Furthermore, shill bidding does not leave behind any apparent evidence, and it is relatively easy to use to cheat innocent buyers. Our goal is to develop a classification model that is capable of efficiently differentiating between legitimate bidders and shill bidders. For our study, we employ an actual training dataset, but the data are unlabeled. First, we properly label the shill bidding samples by combining a robust hierarchical clustering technique and a semi-automated labeling approach. Since shill bidding datasets are imbalanced, we assess advanced over-sampling, under-sampling and hybrid-sampling methods and compare their performances based on several classification algorithms. The optimal shill bidding classifier displays high detection and low misclassification rates of fraudulent activities. |
author |
Anowar,Farzana Sadaoui,Samira |
author_facet |
Anowar,Farzana Sadaoui,Samira |
author_sort |
Anowar,Farzana |
title |
Detection of Auction Fraud in Commercial Sites |
title_short |
Detection of Auction Fraud in Commercial Sites |
title_full |
Detection of Auction Fraud in Commercial Sites |
title_fullStr |
Detection of Auction Fraud in Commercial Sites |
title_full_unstemmed |
Detection of Auction Fraud in Commercial Sites |
title_sort |
detection of auction fraud in commercial sites |
publisher |
Universidad de Talca |
publishDate |
2020 |
url |
http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762020000100107 |
work_keys_str_mv |
AT anowarfarzana detectionofauctionfraudincommercialsites AT sadaouisamira detectionofauctionfraudincommercialsites |
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1714202236281683968 |