Affiliations: [a] School of Computer Science, Carleton University, Ottawa, ON, Canada. E-mail: paulv@scs.carleton.ca | [b] Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada. E-mail: julie.thorpe@uoit.ca
Note: [*] Manuscript received November 7, 2008; revised July 28, 2010; accepted August 4, 2010. Parts of this
work appeared previously in [41] and in the PhD thesis [40] of the second author.
Abstract: We provide an in-depth study of the security of click-based graphical password schemes like PassPoints (Weidenbeck et al., 2005), by exploring popular points (hot-spots), and examining strategies to predict and exploit them in guessing attacks. We report on both short- and long-term user studies: one lab-controlled, involving 43 users and 17 diverse images, the other a field test of 223 user accounts. We provide empirical evidence that hot-spots do exist for many images, some more so than others. We explore the use of “human-computation” (in this context, harvesting click-points from a small set of users) to predict these hot-spots. We generate two “human-seeded” attacks based on this method: one based on a first-order Markov model, another based on an independent probability model. Within 100 guesses, our first-order Markov model-based attack finds 4% of passwords in one image's data set, and 10% of passwords in a second image's data set. Our independent model-based attack finds 20% within 233 guesses in one image's data set and 36% within 231 guesses in a second image's data set. These are all for a system whose full password space has cardinality 243. We evaluate our first-order Markov model-based attack with cross-validation of the field study data, which finds an average of 7–10% of user passwords within 3 guesses. We also begin to explore some click-order pattern attacks, which we found improve on our independent model-based attacks. Our results suggest that these graphical password schemes (with parameters as originally proposed) are vulnerable to offline and online attacks, even on systems that implement conservative lock-out policies.
Keywords: Graphical passwords, PassPoints, passwords, hot spots, human-seeded attacks, human computation, click-order patterns, password guessing, dictionary attack, empirical studies, user choice
