Biblio

Despite their ubiquity, many password meters provide inaccurate strength estimates. Furthermore, they do not explain to users what is wrong with their password or how to improve it. We describe the development and evaluation of a data-driven password meter that provides accurate strength measurement and actionable, detailed feedback to users. This meter combines neural networks and numerous carefully combined heuristics to score passwords and generate data-driven text feedback about the user's password. We describe the meter's iterative development and final design. We detail the security and usability impact of the meter's design dimensions, examined through a 4,509-participant online study. Under the more common password-composition policy we tested, we found that the data-driven meter with detailed feedback led users to create more secure, and no less memorable, passwords than a meter with only a bar as a strength indicator.

Despite considerable research on passwords, empirical studies of password strength have been limited by lack of access to plaintext passwords, small data sets, and password sets specifically collected for a research study or from low-value accounts. Properties of passwords used for high-value accounts thus remain poorly understood. We fill this gap by studying the single-sign-on passwords used by over 25,000 faculty, staff, and students at a research university with a complex password policy. Key aspects of our contributions rest on our (indirect) access to plaintext passwords. We describe our data collection methodology, particularly the many precautions we took to minimize risks to users. We then analyze how guessable the collected passwords would be during an offline attack by subjecting them to a state-of-the-art password cracking algorithm. We discover significant correlations between a number of demographic and behavioral factors and password strength. For example, we find that users associated with the computer science school make passwords more than 1.5 times as strong as those of users associated with the business school. while users associated with computer science make strong ones. In addition, we find that stronger passwords are correlated with a higher rate of errors entering them. We also compare the guessability and other characteristics of the passwords we analyzed to sets previously collected in controlled experiments or leaked from low-value accounts. We find more consistent similarities between the university passwords and passwords collected for research studies under similar composition policies than we do between the university passwords and subsets of passwords leaked from low-value accounts that happen to comply with the same policies.

We present an architecture for the Security Behavior Observatory (SBO), a client-server infrastructure designed to collect a wide array of data on user and computer behavior from hundreds of participants over several years. The SBO infrastructure had to be carefully designed to fulfill several requirements. First, the SBO must scale with the desired length, breadth, and depth of data collection. Second, we must take extraordinary care to ensure the security of the collected data, which will inevitably include intimate participant behavioral data. Third, the SBO must serve our research interests, which will inevitably change as collected data is analyzed and interpreted. This short paper summarizes some of our design and implementation benefits and discusses a few hurdles and trade-offs to consider when designing such a data collection system.