WordMarkov: A New Password Probability Model of Semantics

Jiahong  Xie; Haibo  Cheng; Rong  Zhu; Ping Wang; Kaitai Liang

doi:10.1109/ICASSP43922.2022.9746203

WordMarkov: A New Password Probability Model of Semantics

Jiahong Xie, Haibo Cheng, Rong Zhu, Ping Wang, Kaitai Liang

Cyber Security

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

2 Citations (Scopus)

15 Downloads (Pure)

Abstract

To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on "long" passwords, indicating the importance of semantic parts in long passwords.

Original language	English
Title of host publication	Proceedings of the ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Place of Publication	Piscataway
Publisher	IEEE
Pages	3034-3038
Number of pages	5
ISBN (Electronic)	978-1-6654-0540-9
ISBN (Print)	978-1-6654-0541-6
DOIs	https://doi.org/10.1109/ICASSP43922.2022.9746203
Publication status	Published - 2022
Event	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) - Singapore, Singapore Duration: 23 May 2022 → 27 May 2022

Conference

Conference	ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Country/Territory	Singapore
City	Singapore
Period	23/05/22 → 27/05/22

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

Markov Chain
password probability model
word segmentation
semantic information of password

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WordMarkov_A_New_Password_Probability_Model_of_SemanticsFinal published version, 2.3 MB

Cite this

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title = "WordMarkov: A New Password Probability Model of Semantics",

abstract = "To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on {"}long{"} passwords, indicating the importance of semantic parts in long passwords.",

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author = "Jiahong Xie and Haibo Cheng and Rong Zhu and Ping Wang and Kaitai Liang",

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Xie, J, Cheng, H, Zhu, R, Wang, P & Liang, K 2022, WordMarkov: A New Password Probability Model of Semantics. in Proceedings of the ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)., 9746203, IEEE, Piscataway, pp. 3034-3038, ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Singapore, Singapore, 23/05/22. https://doi.org/10.1109/ICASSP43922.2022.9746203

WordMarkov: A New Password Probability Model of Semantics. / Xie, Jiahong ; Cheng, Haibo ; Zhu, Rong et al.
Proceedings of the ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Piscataway: IEEE, 2022. p. 3034-3038 9746203.

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

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T2 - ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

AU - Xie, Jiahong

AU - Cheng, Haibo

AU - Zhu, Rong

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.

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N2 - To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on "long" passwords, indicating the importance of semantic parts in long passwords.

AB - To date there are few researches on the semantic information of passwords, which leaves a gap preventing us from fully understanding the passwords characteristic and security. We propose a new password probability model for semantic information based on Markov Chain with both generalization and accuracy, called WordMarkov, that can capture the semantic essence of password samples. Further, we evaluate our design via password guessing attacks, on six real-world datasets, and we show that WordMarkov obtains 24.29%–67.37% improvement over the state-of-the-art password probability models. Even more surprising is that WordMarkov achieves 75.35%–96.34% attack improvement on "long" passwords, indicating the importance of semantic parts in long passwords.

KW - Markov Chain

KW - password probability model

KW - word segmentation

KW - semantic information of password

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BT - Proceedings of the ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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ER -

WordMarkov: A New Password Probability Model of Semantics

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Keywords

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