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When Privacy meets Security: Leveraging personal information for password cracking
[article]
2013
arXiv
pre-print
Passwords are widely used for user authentication and, despite their weaknesses, will likely remain in use in the foreseeable future. Human-generated passwords typically have a rich structure, which makes them susceptible to guessing attacks. In this paper, we study the effectiveness of guessing attacks based on Markov models. Our contributions are two-fold. First, we propose a novel password cracker based on Markov models, which builds upon and extends ideas used by Narayanan and Shmatikov
arXiv:1304.6584v1
fatcat:zr5lggqmhrdv3i2ukp3vmvpdxm