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2015 | OriginalPaper | Buchkapitel

Predicting Graphical Passwords

verfasst von : Matthieu Devlin, Jason R. C. Nurse, Duncan Hodges, Michael Goldsmith, Sadie Creese

Erschienen in: Human Aspects of Information Security, Privacy, and Trust

Verlag: Springer International Publishing

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Abstract

Over the last decade, the popularity of graphical passwords has increased tremendously. They can now be found on various devices and systems, including platforms such as the Windows 8 and Android operating systems. In this paper, we focus on the PassPoints graphical-password scheme and investigate the extent to which these passwords might be predicted based on knowledge of the individual (e.g., their age, gender, education, learning style). We are particularly interested in understanding whether graphical passwords may suffer the same weaknesses as textual passwords, which are often strongly correlated with an individual using memorable information (such as the individuals spouses, pets, preferred sports teams, children, and so on). This paper also introduces a novel metric for graphical-password strength to provide feedback to an individual without the requirement of knowing the image or having password statistics a priori.

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Fußnoten
1
‘Marton Mere Swimming Pool’ by havenholidays (https://​flic.​kr/​p/​4ycWeu).
 
2
‘One of the Glens, Scotland’ by Chris Ford (https://​flic.​kr/​p/​8BumLU).
 
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Metadaten
Titel
Predicting Graphical Passwords
verfasst von
Matthieu Devlin
Jason R. C. Nurse
Duncan Hodges
Michael Goldsmith
Sadie Creese
Copyright-Jahr
2015
DOI
https://doi.org/10.1007/978-3-319-20376-8_3