WordMarkov

A New Password Probability Model of Semantics

Conference paper (2022)

Authors

Jiahong Xie Peking University
Haibo Cheng Peking University
Rong Zhu Peking University
Ping Wang Peking University
K. Liang Cyber Security - EEMCS
Research Group
Cyber Security (EEMCS) (TU Delft)
Copyright: © 2022 Jiahong Xie, Haibo Cheng, Rong Zhu, Ping Wang, K. Liang
DOI
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https://doi.org/10.1109/ICASSP43922.2022.9746203
Markov Chain Password probability model Word segmentation Semantic information of password
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Published Date

2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Cyber Security

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.