Author
Listed:
- Mahdi Nasrullah Al-Ameen
- Sonali T. Marne
- Kanis Fatema
- Matthew Wright
- Shannon Scielzo
Abstract
User-chosen passwords reflecting common strategies and patterns ease memorisation but offer uncertain and often weak security, while system-assigned passwords provide higher security guarantee but suffer from poor memorability. We thus examine the technique to enhance password memorability that incorporates a scientific understanding of long-term memory. In particular, we examine the efficacy of providing users with verbal cues—real-life facts corresponding to system-assigned keywords. We also explore the usability gain of including images related to the keywords along with verbal cues. In our multi-session lab study with 52 participants, textual recognition-based scheme offering verbal cues had a significantly higher login success rate (94.23%) compared to the control condition, i.e. textual recognition without verbal cues (61.54%). When users were provided with verbal cues, adding images contributed to faster recognition of the assigned keywords, and thus had an overall improvement in usability. So, we conducted a field study with 54 participants to further examine the usability of graphical recognition-based scheme offering verbal cues, which showed an average login success rate of 98% in a real-life setting and an overall improvement in login performance with more login sessions. These findings show a promising research direction to gain high memorability for system-assigned passwords.
Suggested Citation
Mahdi Nasrullah Al-Ameen & Sonali T. Marne & Kanis Fatema & Matthew Wright & Shannon Scielzo, 2022.
"On improving the memorability of system-assigned recognition-based passwords,"
Behaviour and Information Technology, Taylor & Francis Journals, vol. 41(5), pages 1115-1131, April.
Handle:
RePEc:taf:tbitxx:v:41:y:2022:i:5:p:1115-1131
DOI: 10.1080/0144929X.2020.1858161
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:tbitxx:v:41:y:2022:i:5:p:1115-1131. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/tbit .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.