NDLI: A Study of Probabilistic Password Models

Content Provider	IEEE Xplore Digital Library
Author	Ma, J. Weining Yang Min Luo Ninghui Li
Copyright Year	2014
Abstract	A probabilistic password model assigns a probability value to each string. Such models are useful for research into understanding what makes users choose more (or less) secure passwords, and for constructing password strength meters and password cracking utilities. Guess number graphs generated from password models are a widely used method in password research. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. We conduct a systematic evaluation of a large number of probabilistic password models, including Markov models using different normalization and smoothing methods, and found that, among other things, Markov models, when done correctly, perform significantly better than the Probabilistic Context-Free Grammar model proposed in Weir et al., which has been used as the state-of-the-art password model in recent research.
Starting Page	689
Ending Page	704
File Size	1816179
Page Count	16
File Format	PDF
ISBN	9781479946860
ISSN	10816011
DOI	10.1109/SP.2014.50
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-05-18
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Markov processes Computational modeling Testing Training Educational institutions Dictionaries Probabilistic logic
Content Type	Text
Resource Type	Article

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