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Are Swedish Passwords Tougher Than the Rest?
Linköping University, Department of Computer and Information Science. Linköping University, Faculty of Science & Engineering. Swedish Defence Research Agency (FOI), Linköping, Sweden.
Swedish Defence Research Agency (FOI), Linköping, Sweden.
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: 0000-0002-9829-9287
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2024 (English)In: 29th Nordic Conference, NordSec 2024 Karlstad, Sweden, November 6–7, 2024 Proceedings, Springer, 2024Conference paper, Published paper (Refereed)
Abstract [en]

In today’s digital world, passwords are the keys that unlock our online lives, keeping our social media, financial accounts, and streaming services secure. However, creating strong passwords that are easy to remember is hard. Often an individual’s language and cultural background influence password creation, which an attacker can exploit. This paper examines the importance of the User Context Bias (UCB) on Swedish passwords, versus international passwords stemming from multiple languages and cultures. This is done by employing four different password-cracking tools; Probabilistic Context Free Grammar (PCFG), Ordered Markov Enumerator (OMEN), Odinn, and Hashcat. The findings reveal that all the tools are able to crack a higher percentage of passwords when attacking those created by Swedish natives compared to their international counterparts. PCFG, in particular, is nearly twice as effective as the other tools against Swedish passwords after just 10,000 guesses, while OMEN is the best against Swedish passwords after 5 million guesses.

Place, publisher, year, edition, pages
Springer, 2024.
Series
Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349
Series
Secure IT Systems, E-ISSN 1611-3349
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:liu:diva-211513DOI: 10.1007/978-3-031-79007-2_1ISI: 001446544900001Scopus ID: 2-s2.0-85218501734ISBN: 9783031790065 (print)ISBN: 9783031790072 (electronic)OAI: oai:DiVA.org:liu-211513DiVA, id: diva2:1935199
Conference
29th Nordic Conference, NordSec 2024 Karlstad, Sweden, November 6–7, 2024
Available from: 2025-02-06 Created: 2025-02-06 Last updated: 2025-04-17

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Singh Gaba, GurjotGurtov, Andrei

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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • de-DE
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Output format
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