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Phishing in Style: Characterizing Phishing Websites in the Wild
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0002-7631-0625
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.
Linköping University, Department of Computer and Information Science, Database and information techniques. Linköping University, Faculty of Science & Engineering.ORCID iD: 0000-0003-1367-1594
2023 (English)In: Proc. Network Traffic Measurement and Analysis Conference (TMA), Institute of Electrical and Electronics Engineers (IEEE), 2023Conference paper, Published paper (Refereed)
Abstract [en]

The prevalence of phishing domains is steadily rising as attackers exploit toolkits to create phishing websites. As web development expertise is no longer a prerequisite, phishing attacks have become more widespread, outpacing many existing detection methods. Developing novel techniques to identify malicious domains is crucial to safeguard potential victims online. While most current methods emphasize the visual aspects of phishing websites, in this paper, we investigate the underlying structure by collecting data on style sheets and certificates from both verified phishing domains and benign domains. Using a token-based similarity algorithm, we group the phishing domains into three categories and identify shared characteristics of these domains. Our work demonstrates the feasibility of using structural similarities to identify a website created using a phishing kit. By employing such detection, users would be able to browse the web with a reduced risk of falling victim to malicious activities.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers (IEEE), 2023.
Series
2023 7th Network Traffic Measurement and Analysis Conference (TMA)
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:liu:diva-199092DOI: 10.23919/TMA58422.2023.10199059Scopus ID: 2-s2.0-85168760335ISBN: 9783903176584 (electronic)ISBN: 9798350325676 (print)OAI: oai:DiVA.org:liu-199092DiVA, id: diva2:1811236
Conference
Proc. Network Traffic Measurement and Analysis Conference (TMA), Napoli, Italy, 26-29 June, 2023
Available from: 2023-11-11 Created: 2023-11-11 Last updated: 2023-11-16Bibliographically approved

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Hasselquist, DavidCarlsson, Niklas

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Hasselquist, DavidKihlberg Gawell, ElsaKarlström, AxelCarlsson, Niklas
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Total: 66 hits
CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
Language
  • de-DE
  • en-GB
  • en-US
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  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
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