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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Autores principales: Anowar,Farzana, Sadaoui,Samira
Lenguaje:English
Publicado: Universidad de Talca 2020
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Acceso en línea:http://www.scielo.cl/scielo.php?script=sci_arttext&pid=S0718-18762020000100107
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spelling 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
institution Scielo Chile
collection Scielo Chile
language English
topic Auction fraud
Fraud detection
Shill bidding
Data clustering
Data labeling
Data sampling
supervised classification
spellingShingle 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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