Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction

Haibo  Cheng; Wenting Li; Ping Wang; Kaitai  Liang

doi:10.1109/ICASSP39728.2021.9414886

Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction

Haibo Cheng, Wenting Li, Ping Wang, Kaitai Liang

Cyber Security

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

2 Citations (Scopus)

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.

Original language	English
Title of host publication	ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Place of Publication	Piscataway
Publisher	IEEE
Pages	2690-2694
Number of pages	5
ISBN (Electronic)	978-1-7281-7605-5
ISBN (Print)	978-1-7281-7606-2
DOIs	https://doi.org/10.1109/ICASSP39728.2021.9414886
Publication status	Published - 2021
Event	ICASSP 2021: The IEEE International Conference on Acoustics, Speech, and Signal Processing - Virtual Conference/Toronto, Canada Duration: 6 Jun 2021 → 11 Jun 2021

Conference

Conference	ICASSP 2021
Country/Territory	Canada
City	Virtual Conference/Toronto
Period	6/06/21 → 11/06/21

Keywords

Password
Probabilistic context-free grammar
Word extraction

Access to Document

10.1109/ICASSP39728.2021.9414886

Cite this

@inproceedings{9bb0484305fe440780fa57c97756ffdc,

title = "Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction",

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.",

keywords = "Password, Probabilistic context-free grammar, Word extraction",

author = "Haibo Cheng and Wenting Li and Ping Wang and Kaitai Liang",

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booktitle = "ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE ",

address = "United States",

note = "ICASSP 2021 : The IEEE International Conference on Acoustics, Speech, and Signal Processing ; Conference date: 06-06-2021 Through 11-06-2021",

}

Cheng, H, Li, W, Wang, P & Liang, K 2021, Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction. in ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., 9414886, IEEE , Piscataway, pp. 2690-2694, ICASSP 2021, Virtual Conference/Toronto, Canada, 6/06/21. https://doi.org/10.1109/ICASSP39728.2021.9414886

Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction. / Cheng, Haibo ; Li, Wenting; Wang, Ping; Liang, Kaitai .

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway : IEEE , 2021. p. 2690-2694 9414886.

Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review

TY - GEN

T1 - Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction

AU - Cheng, Haibo

AU - Li, Wenting

AU - Wang, Ping

AU - Liang, Kaitai

PY - 2021

Y1 - 2021

N2 - 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.

AB - 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.

KW - Password

KW - Probabilistic context-free grammar

KW - Word extraction

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U2 - 10.1109/ICASSP39728.2021.9414886

DO - 10.1109/ICASSP39728.2021.9414886

M3 - Conference contribution

SN - 978-1-7281-7606-2

SP - 2690

EP - 2694

BT - ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

CY - Piscataway

T2 - ICASSP 2021

Y2 - 6 June 2021 through 11 June 2021

ER -

Improved Probabilistic Context-Free Grammars for Passwords Using Word Extraction

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