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Analysis of third-party request structures to detect fraudulent websites

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Gopal, Ram D., Hojati, Afrouz and Patterson, Raymond A. (2022) Analysis of third-party request structures to detect fraudulent websites. Decision Support Systems, 154 . 113698. doi:10.1016/j.dss.2021.113698 ISSN 0167-9236.

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Official URL: https://doi.org/10.1016/j.dss.2021.113698

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Abstract

Third-party websites or applications are the key entities in the web eco-system that enable websites to function and o er services. Almost every organization today uses dozens of websites and sub-domains. Each provides essential func- tions and typically uses dozens of third-parties to produce its capabilities. With the growing problem of illegitimate websites, such as those peddling fake news and selling counterfeit products, the detection of fraudulent websites becomes more and more crucial. While the conventional method of fraudulent website detection mostly relies on the content-based analysis of websites, the method of this study uses third-party request structure features and attributes of third- parties engaged in the structure to predict legitimate and fraudulent websites. This method can be used on a real-time basis to complement current detection methods. Moreover, our approach is not limited to a speci c category of web- sites. In other words, unlike previous studies, our approach is able to increase the likelihood of detecting all kinds of fake and fraudulent websites. The results of this study are largely robust across di erent predictive models.

Item Type: Journal Article
Subjects: H Social Sciences > HV Social pathology. Social and public welfare
Q Science > QA Mathematics
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Social Sciences > Warwick Business School > Information Systems & Management
Faculty of Social Sciences > Warwick Business School
Library of Congress Subject Headings (LCSH): Computer crimes -- Prevention, Phishing, Internet fraud, Web sites -- Corrupt practices, Anomaly detection (Computer security)
Journal or Publication Title: Decision Support Systems
Publisher: Elsevier BV
ISSN: 0167-9236
Official Date: March 2022
Dates:
DateEvent
March 2022Published
20 November 2021Available
16 November 2021Accepted
Volume: 154
Article Number: 113698
DOI: 10.1016/j.dss.2021.113698
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 17 November 2021
Date of first compliant Open Access: 20 May 2023
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDUniversity of Calgaryhttp://dx.doi.org/10.13039/100008459
UNSPECIFIEDUniversity of Warwickhttp://dx.doi.org/10.13039/501100000741
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