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LPG-PCFG: An Improved Probabilistic Context- Free Grammar to Hit Low-Probability Passwords.

Xiaozhou Guo1,2, Kaijun Tan1,2, Yi Liu1,2

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

Sensors (Basel, Switzerland)
|June 24, 2022
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Summary

This study introduces a new method to generate low-probability passwords, significantly improving offline password attack efficiency. The novel approach enhances the discovery rate of hard-to-crack, yet semantically relevant, password combinations.

Keywords:
PCFGdegeneration distributioninformation securitylow-probability passwordpassword-generation model

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Area of Science:

  • Computer Science
  • Information Security
  • Cryptography

Background:

  • Password authentication is crucial for internet security.
  • Current password generation models struggle to effectively create or identify low-probability passwords, hindering security analysis.
  • Offline password attacks benefit from improved generation of low-probability passwords.

Purpose of the Study:

  • To develop a method for efficiently generating low-probability passwords.
  • To enhance the effectiveness of offline password attacks and password strength evaluations.
  • To address the limitations of existing password generation models in producing semantically meaningful, low-probability passwords.

Main Methods:

  • Proposed a low-probability generator-probabilistic context-free grammar (LPG-PCFG) model.
  • Utilized a control variable method to fine-tune LPG-PCFG degeneration parameters.
  • Compared LPG-PCFG against a non-degeneration PCFG model.

Main Results:

  • LPG-PCFG successfully increases the probability of generating low-probability passwords.
  • Optimal degeneration parameters for LPG-PCFG were identified.
  • Generated a significantly larger number of low-probability password hits compared to the standard PCFG model, with increases of 50.4% and 42.0% at 10^7 and 10^8 generations, respectively.

Conclusions:

  • LPG-PCFG is an effective method for generating low-probability passwords.
  • The proposed model improves the efficiency of offline password attacks.
  • This research contributes to better understanding and evaluating password security by focusing on the generation of less probable, yet structured, password candidates.