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Password Guessing via Neural Language Modeling

  • Hang Li
  • Mengqi Chen
  • Shengbo Yan
  • Chunfu JiaEmail author
  • Zhaohui Li
Conference paper
  • 658 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11806)

Abstract

Passwords are the major part of authentication in current social networks. The state-of-the-art password guessing approaches, such as Markov model and probabilistic context-free grammars (PCFG) model, assign a probability value to each password by a statistic approach without any parameters. These methods require large datasets to accurately estimate probability due to the law of large number. The neural network, approximating target probability distribution through iteratively training its parameters, was used to model passwords by some researches. However, since the network architectures they used are simple and straightforward, there are many ways to improve it.

In this paper, we view password guessing as a language modeling task and introduce a deeper, more robust, and faster-converged model with several useful techniques to model passwords. This model shows great ability in modeling passwords while significantly outperforms state-of-the-art approaches. Inspired by the most advanced sequential model named Transformer, we use it to model passwords with bidirectional masked language model which is powerful but unlikely to provide normalized probability estimation. Then we distill Transformer model’s knowledge into our proposed model to further boost its performance. Comparing with the PCFG, Markov and previous neural network models, our models show remarkable improvement in both one-site tests and cross-site tests. Moreover, our models are robust to the password policy by controlling the entropy of output distribution.

Keywords

Authentication Password guessing Neural network 

Notes

Acknowledgment

The authors are grateful to the anonymous reviewers for their constructive comments. This work was supported in part by the National Natural Science Foundation of China under Grant 61702399 and Grant 61772291 and Grant 61972215 in part by the Natural Science Foundation of Tianjin, China, under Grant 17JCZDJC30500.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Hang Li
    • 1
    • 3
  • Mengqi Chen
    • 2
    • 3
  • Shengbo Yan
    • 2
    • 3
  • Chunfu Jia
    • 2
    • 3
    Email author
  • Zhaohui Li
    • 2
    • 3
  1. 1.College of Artificial IntelligenceNankai UniversityTianjinChina
  2. 2.College of Cyber ScienceNankai UniversityTianjinChina
  3. 3.Tianjin Key Laboratory of Network and Data SecurityNankai UniversityTianjinChina

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