Improved Wordpcfg for Passwords with Maximum Probability Segmentation

Conference paper (2023)

Authors

Wenting Li Peking University

Jiahong Yang Peking University

Haibo Cheng Peking University

Ping Wang Peking University

K. Liang

DOI: https://doi.org/10.1109/ICASSP49357.2023.10096535

Password Probabilistic context-free grammar Maximum probability segmentation

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Published Date

2023

Language

English

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Abstract

Modeling password distributions is a fundamental problem in password security, benefiting the research and applications on password guessing, password strength meters, honey password vaults, etc. As one of the best segment-based password models, WordPCFG has been proposed to capture individual semantic segments (called words) in passwords. However, we find WordPCFG does not address well the ambiguity of password segmentation by maximum matching, leading to the unreasonable segmentation of many password and further the inaccuracy of modeling password distributions. To address the ambiguity, we improve WordPCFG by maximum probability segmentation with A*-like pruning algorithm. The experimental results show that the improved WordPCFG cracks 99.26%–99.95% passwords, with nearly 5.67%–18.01% improvement.

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