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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. (2021) Analysis of third-party request structures to detect fraudulent websites. Decision Support Systems . 113698. doi:10.1016/j.dss.2021.113698 (In Press)
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Official URL: https://doi.org/10.1016/j.dss.2021.113698
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 | |||||||||
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| Subjects: | H Social Sciences > HV Social pathology. Social and public welfare Q Science > QA Mathematics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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| Divisions: | Faculty of Social Sciences > Warwick Business School > Information Systems & Management Faculty of Social Sciences > Warwick Business School |
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| 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: | 2021 | |||||||||
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| Article Number: | 113698 | |||||||||
| DOI: | 10.1016/j.dss.2021.113698 | |||||||||
| Status: | Peer Reviewed | |||||||||
| Publication Status: | In Press | |||||||||
| Access rights to Published version: | Restricted or Subscription Access | |||||||||
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