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PassTCN-PPLL: A Password Guessing Model Based on Probability Label Learning and Temporal Convolutional Neural

Junbin Ye1,2, Min Jin1,3, Guoliang Gong1,3

  • 1Institute of Semiconductors, Chinese Academy of Sciences, Beijing 100083, China.

Sensors (Basel, Switzerland)
|September 9, 2022
PubMed
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This study introduces PassTCN-PPLL, a novel deep learning model for efficient password guessing. It significantly improves password generation coverage and reduces repetition rates compared to existing methods.

Area of Science:

  • Computer Science
  • Cybersecurity
  • Artificial Intelligence

Background:

  • Password leakage incidents necessitate advanced password security research.
  • Password guessing is crucial for cracking and security analysis.
  • Deep learning offers potential for efficient password guessing, but current models (RNN, GAN, VAE) suffer from low efficiency and high repetition.

Purpose of the Study:

  • To propose an improved password-guessing model using temporal convolutional neural networks (PassTCN).
  • To enhance generated password performance through a novel password probability label-learning method.
  • To address the limitations of existing deep learning models in password guessing.

Main Methods:

  • Developed a password-guessing model named PassTCN based on temporal convolutional neural networks.
Keywords:
password guessingpassword labeltemporal convolutional neural network

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  • Introduced a password probability label-learning (PPLL) method to reconstruct labels based on training set probability distributions.
  • Implemented training set deduplication to further optimize the model.

    • Main Results:

    • PassTCN-PPLL achieved a 12.6% coverage rate for 108 generated passwords on the RockYou dataset.
    • Demonstrated significant improvements in coverage rate: 87.2% over PassGAN, 72.6% over VAEPass, and 42.9% over FLA.
    • Achieved a 25.9% repetition rate, outperforming PassGAN (45.1% lower), VAEPass (31.7% lower), and FLA (17.4% lower).

    Conclusions:

    • The proposed PassTCN-PPLL model effectively enhances password guessing efficiency.
    • The novel password probability label-learning method is key to improving coverage and reducing repetition.
    • This approach offers a superior alternative to existing deep learning models for password security research.