Predicting graphical passwords

Conference item

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.

Authors: Devlin, M; Nurse, JRC; Hodges, D; Goldsmith, M; Creese, S

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: Springer International Publishing
• Host title: Human Aspects of Information Security, Privacy, and Trust : Third International Conference, HAS 2015, Held as Part of HCI International 2015, Los Angeles, CA, USA, August 2-7, 2015. Proceedings
• Pages: 23-35
• Series: Lecture Notes in Computer Science
• Series number: 9190
• Publication date: 2015-07-21
• Edition: Accepted Manuscript
• Event title: HAS 2015: Human Aspects of Information Security, Privacy, and Trust
• Event location: Los Angeles, CA, USA
• Event website: http://2015.hci.international/has
• Event start date: 2015-08-02
• Event end date: 2015-08-07
• DOI: 10.1007/978-3-319-20376-8_3
• ISSN: 0302-9743
• EISBN: 978-3-319-20376-8
• ISBN: 978-3-319-20375-1
• Language: English
• Keywords: user characteristics; usable security; PassPoints scheme; graphical passwords; password-strength metric
• Subjects: Internet research; Computing