Corpus ID: 2320797

Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks

@inproceedings{Melicher2016FastLA,
  title={Fast, Lean, and Accurate: Modeling Password Guessability Using Neural Networks},
  author={William Melicher and B. Ur and Saranga Komanduri and L. Bauer and N. Christin and L. Cranor},
  booktitle={USENIX Annual Technical Conference},
  year={2016}
}
  • William Melicher, B. Ur, +3 authors L. Cranor
  • Published in
    USENIX Annual Technical…
    2016
  • Computer Science
  • Human-chosen text passwords, today's dominant form of authentication, are vulnerable to guessing attacks. [...] Key Method We show that neural networks can often guess passwords more effectively than state-of-the-art approaches, such as probabilistic context-free grammars and Markov models. We also show that our neural networks can be highly compressed-to as little as hundreds of kilobytes-without substantially worsening guessing effectiveness. Building on these results, we implement in JavaScript the first…Expand Abstract
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