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Generating borderline test samples for randomness testers via intelligent optimization and evolutionary algorithms.

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Summary
This summary is machine-generated.

This study introduces a new framework for generating test data to ensure high-quality random sequences for information security. It uses an evolutionary algorithm and a large language model to create challenging test data for randomness testers.

Keywords:
Genetic algorithmLarge language modelMulti-objective optimizationRandom number generationRandomness testing

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Area of Science:

  • Information Security
  • Cryptography
  • Computational Intelligence

Background:

  • High-quality random sequences are crucial for encryption keys in information security.
  • Physical entropy sources for true random number generation are vulnerable to environmental factors, requiring real-time randomness testing.
  • Existing test data generation methods struggle with creating sequences that precisely fail randomness criteria or violate multiple criteria simultaneously.

Purpose of the Study:

  • To develop a dynamic test data generation framework addressing limitations in current methods.
  • To create challenging, borderline sequences for real-time randomness testers.
  • To improve the reliability and security of encryption key generation.

Main Methods:

  • Utilizes an evolutionary algorithm (EA) to frame borderline sequence generation as a multi-constrained optimization problem.
  • Employs a large language model (LLM) as a dynamic parameter adjuster, analyzing evolutionary trends and using game-theoretic mechanisms.
  • The LLM adaptively tunes parameters, mitigating dimensionality issues in multi-objective optimization for real-time adjustments.

Main Results:

  • The framework successfully generates borderline test data that slightly fail randomness criteria but maintain statistical similarity to high-entropy sources.
  • Generated sequences are fault-detectable and serve as realistic, challenging inputs for statistical-test-based randomness testers.
  • Demonstrates effective mitigation of the curse of dimensionality in multi-objective optimization.

Conclusions:

  • The proposed dynamic test data generation framework enhances the testing of real-time randomness testers.
  • The integration of EA and LLM provides an adaptive and effective approach to generating high-quality, challenging test data.
  • This method strengthens the security of encryption by improving the validation of random number generators.