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Updated: Sep 13, 2025

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
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Cross-Domain Feature Enhancement-Based Password Guessing Method for Small Samples.

Cheng Liu1,2,3, Junrong Li4, Xiheng Liu4

  • 1School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.

Entropy (Basel, Switzerland)
|July 29, 2025
PubMed
Summary

This study introduces a novel small-sample password guessing technique using probabilistic context-free grammar (PCFG) to overcome data limitations. The method enhances cross-domain features, improving password guessing accuracy by up to 10.52%.

Keywords:
password guessingprobabilistic context-free grammarsimilarity computationsmall samples

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Information Security

Background:

  • Password guessing is vital for account protection and intrusion detection.
  • Traditional models require large datasets, but privacy regulations limit data access.
  • This creates a challenge for researchers needing to guess passwords from small sets.

Purpose of the Study:

  • To develop a small-sample password guessing technique enhancing cross-domain features.
  • To address the limitations of traditional models in privacy-constrained environments.
  • To improve the efficiency and accuracy of password guessing with limited data.

Main Methods:

  • Analyzed password sets using probabilistic context-free grammar (PCFG) to derive structure and fragment probabilities.
  • Generated password set structure vectors for similarity comparison using cosine similarity.
  • Enhanced small-sample password set features by modifying training set structure vectors.

Main Results:

  • Similarity measurement between small and large password sets is reliable for sets larger than 150 samples.
  • Higher similarity between leaked and target password sets correlates with increased hit rates.
  • The proposed feature enhancement method improved hit rates by up to 10.52% for small-sample sets.

Conclusions:

  • The developed technique effectively addresses the challenge of small-sample password guessing.
  • It offers a viable solution without requiring prior knowledge of the target password set.
  • The method enhances security research capabilities in data-limited scenarios.