Improved Wordpcfg for Passwords with Maximum Probability Segmentation

Wenting Li; Jiahong  Yang; Haibo  Cheng; Ping Wang; Kaitai Liang

doi:10.1109/ICASSP49357.2023.10096535

Improved Wordpcfg for Passwords with Maximum Probability Segmentation

Wenting Li, Jiahong Yang, Haibo Cheng, Ping Wang, Kaitai Liang

Cyber Security

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

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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.

Original language	English
Title of host publication	Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Place of Publication	Piscataway
Publisher	IEEE
Number of pages	5
ISBN (Electronic)	978-1-7281-6327-7
ISBN (Print)	978-1-7281-6328-4
DOIs	https://doi.org/10.1109/ICASSP49357.2023.10096535
Publication status	Published - 2023
Event	48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023 - Rhodes Island, Greece Duration: 4 Jun 2023 → 10 Jun 2023

Conference

Conference	48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023
Abbreviated title	ICASSP 2023
Country/Territory	Greece
City	Rhodes Island
Period	4/06/23 → 10/06/23

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care
Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

Password
Probabilistic context-free grammar
maximum probability segmentation

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10.1109/ICASSP49357.2023.10096535

Improved_Wordpcfg_for_Passwords_with_Maximum_Probability_SegmentationFinal published version, 1.16 MB

Cite this

@inproceedings{03dd18910d724e6b9d6b6f0dc87491ca,

title = "Improved Wordpcfg for Passwords with Maximum Probability Segmentation",

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.",

keywords = "Password, Probabilistic context-free grammar, maximum probability segmentation",

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

note = "Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.; 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, ICASSP 2023 ; Conference date: 04-06-2023 Through 10-06-2023",

year = "2023",

doi = "10.1109/ICASSP49357.2023.10096535",

language = "English",

isbn = " 978-1-7281-6328-4",

booktitle = "Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)",

publisher = "IEEE",

address = "United States",

}

Li, W, Yang, J, Cheng, H, Wang, P & Liang, K 2023, Improved Wordpcfg for Passwords with Maximum Probability Segmentation. in Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, Piscataway, 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023, Rhodes Island, Greece, 4/06/23. https://doi.org/10.1109/ICASSP49357.2023.10096535

Improved Wordpcfg for Passwords with Maximum Probability Segmentation. / Li, Wenting; Yang, Jiahong ; Cheng, Haibo et al.
Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2023.

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

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AU - Li, Wenting

AU - Yang, Jiahong

AU - Cheng, Haibo

AU - Wang, Ping

AU - Liang, Kaitai

N1 - Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

PY - 2023

Y1 - 2023

N2 - 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.

AB - 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.

KW - Password

KW - Probabilistic context-free grammar

KW - maximum probability segmentation

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DO - 10.1109/ICASSP49357.2023.10096535

M3 - Conference contribution

SN - 978-1-7281-6328-4

BT - Proceedings of the ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

PB - IEEE

CY - Piscataway

T2 - 48th IEEE International Conference on Acoustics, Speech and Signal Processing 2023

Y2 - 4 June 2023 through 10 June 2023

ER -

Improved Wordpcfg for Passwords with Maximum Probability Segmentation

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Keywords

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