Supporting Password-Security Decisions with Data
Despite decades of research into developing abstract security advice and improving interfaces, users still struggle to make passwords. Users frequently create passwords that are predictable for attackers or make other decisions (e.g., reusing the same password across accounts) that harm their security. In this thesis, I use data-driven methods to better understand how users choose passwords and how attackers guess passwords. I then combine these insights into a better password-strength meter that provides real-time, data-driven feedback about the user’s candidate password. I first quantify the impact on password security and usability of showing users different passwordstrength meters that score passwords using basic heuristics. I find in a 2,931-participant online study that meters that score passwords stringently and present their strength estimates visually lead users to create stronger passwords without significantly impacting password memorability. Second, to better understand how attackers guess passwords, I perform comprehensive experiments on password-cracking approaches. I find that simply running these approaches in their default configuration is insufficient, but considering multiple well-configured approaches in parallel can serve as a proxy for guessing by an expert in password forensics. The third and fourth sections of this thesis delve further into how users choose passwords. Through a series of analyses, I pinpoint ways in which users structure semantically significant content in their passwords. I also examine the relationship between users’ perceptions of password security and passwords’ actual security, finding that while users often correctly judge the security impact of individual password characteristics, wide variance in their understanding of attackers may lead users to judge predictable passwords as sufficiently strong. Finally, I integrate these insights into an open-source password-strength meter that gives users data-driven feedback about their specific password. I evaluate this meter through a ten-participant laboratory study and 4,509-participant online study.