Hybrid password meters for more secure passwords – a comprehensive study of password meters including nudges and password information

Zimmermann, V., Marky, K. and Renaud, K. (2023) Hybrid password meters for more secure passwords – a comprehensive study of password meters including nudges and password information. Behaviour and Information Technology, 42(6), pp. 700-743. (doi: 10.1080/0144929x.2022.2042384)

Abstract

Supporting users with secure password creation is a well-explored yet unresolved research topic. A promising intervention is the password meter, i.e. providing feedback on the user's password strength as and when it is created. However, findings related to the password meter's effectiveness are varied. An extensive literature review revealed that, besides password feedback, effective password meters often include: (a) feedback nudges to encourage stronger passwords choices and (b) additional guidance. A between-subjects study was carried out with 645 participants to test nine variations of password meters with different types of feedback nudges exploiting various heuristics and norms. This study explored differences in resulting passwords: (1) actual strength, (2) memorability, and (3) user perceptions. The study revealed that password feedback, in combination with a feedback nudge and additional guidance, labelled a hybrid password meter, was generally more efficacious than either intervention on its own, on all three metrics. Yet, the type of feedback nudge targeting either the person, the password creation task, or the social context, did not seem to matter much. The meters were nearly equally efficacious. Future work should explore the long-term effects of hybrid password meters in real-life settings to confirm the external validity of these findings.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Renaud, Professor Karen and Marky, Dr Karola
• Authors: Zimmermann, V., Marky, K., and Renaud, K.
• College/School: College of Science and Engineering > School of Computing Science
• Journal Name: Behaviour and Information Technology
• Publisher: Taylor & Francis
• ISSN: 0144-929X
• ISSN (Online): 1362-3001
• Published Online: 01 March 2022