Purely automated attacks on passpoints-style graphical passwords

  • Authors:

  • Paul C. Van Oorschot;Amirali Salehi-Abari;Julie Thorpe

  • Affiliations:

  • School of Computer Science, Carleton University, Ottawa, ON, Canada;School of Computer Science, Carleton University, Ottawa, ON, Canada;Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada

  • Venue:
  • IEEE Transactions on Information Forensics and Security
  • Year:
  • 2010

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Abstract

We introduce and evaluate various methods for purely automated attacks against PassPoints-style graphical passwords. For generating these attacks, we introduce a graph-based algorithm to efficiently create dictionaries based on heuristics such as click-order patterns (e.g., five points all along a line). Some of our methods combine click-order heuristics with focus-of-attention scan-paths generated from a computational model of visual attention, yielding significantly better automated attacks than previous work. One resulting automated attack finds 7%-16% of passwords for two representative images using dictionaries of approximately 226 entries (where the full password space is 243). Relaxing click-order patterns substantially increased the attack efficacy albeit with larger dictionaries of approximately 235 entries, allowing attacks that guessed 48%-54% of passwords (compared to previous results of 1% and 9% on the same dataset for two images with 235 guesses). These latter attacks are independent of focus-of-attention models, and are based on image-independent guessing patterns. Our results show that automated attacks, which are easier to arrange than human-seeded attacks and are more scalable to systems that use multiple images, require serious consideration when deploying basic PassPoints-style graphical passwords.