Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction

Conference Paper (2021)
Author(s)

Haibo Cheng (Peking University)

Wenting Li (Peking University)

Ping Wang (Ministry of Education, Peking University)

Kaitai Liang (TU Delft - Cyber Security)

Research Group
Cyber Security
DOI related publication
https://doi.org/10.1109/ICASSP39728.2021.9414886
More Info
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Publication Year
2021
Language
English
Research Group
Cyber Security
Pages (from-to)
2690-2694
ISBN (print)
978-1-7281-7606-2
ISBN (electronic)
978-1-7281-7605-5

Abstract

Probabilistic context-free grammars (PCFGs) have been pro-posed to capture password distributions, and further been used in password guessing attacks and password strength meters. However, current PCFGs suffer from the limitation of inaccurate segmentation of password, which leads to misestimation of password probability and thus seriously affects their performance. In this paper, we propose a word extraction approach for passwords, and further present an improved PCFG model, called WordPCFG. The WordPCFG using word extraction method can precisely extract semantic segments (called word) from passwords based on cohesion and freedom of words. We evaluate our WordPCFG on six large-scale datasets, showing that WordPCFG cracks 83.04%–95.47% passwords and obtains 12.96%–71.84% improvement over the state-of-the-art PCFGs.

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