We also observe that research in password modeling can benefit from the extensive literature in statistical language modeling. They are much faster to compute, and at the same time provide information beyond what is feasible in guess-number graphs. In this paper, we show that probability-threshold graphs have important advantages over guess-number graphs. Guess number graphs generated from password models are a widely used method in password research. 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. A probabilistic password model assigns a probability value to each string.
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