Corpus ID: 129999

Measuring Real-World Accuracies and Biases in Modeling Password Guessability

@inproceedings{Ur2015MeasuringRA,
  title={Measuring Real-World Accuracies and Biases in Modeling Password Guessability},
  author={B. Ur and Sean M. Segreti and L. Bauer and N. Christin and L. Cranor and Saranga Komanduri and D. Kurilova and Michelle L. Mazurek and William Melicher and R. Shay},
  booktitle={USENIX Security Symposium},
  year={2015}
}

B. Ur, Sean M. Segreti, +7 authors R. Shay

Published in USENIX Security Symposium 2015

Computer Science

Parameterized password guessability--how many guesses a particular cracking algorithm with particular training data would take to guess a password--has become a common metric of password security. Unlike statistical metrics, it aims to model real-world attackers and to provide per-password strength estimates. We investigate how cracking approaches often used by researchers compare to real-world cracking by professionals, as well as how the choice of approach biases research conclusions. We… CONTINUE READING

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