Abstract
Ensuring information security heavily relies on high-quality random sequences for encryption keys. Physical entropy sources, despite their use in generating true random sequences, are susceptible to environmental disturbances, necessitating real-time randomness testing to maintain high entropy. However, existing methods for generating test data for real-time randomness testers face significant challenges, including producing sequences that fail to meet specific randomness criteria, constructing borderline sequences with slight non-randomness, and addressing the difficulty of simultaneously violating multiple randomness criteria. This paper introduces a dynamic test data generation framework designed to address these challenges. The framework leverages evolutionary algorithm (EA) to transform the generation of borderline sequences into a multi-constrained optimization problem, where a large language model (LLM) acts as a dynamic parameter adjuster. By analyzing evolutionary trends in population statistics and interacting with evolutionary dynamics through a game-theoretic mechanism, the LLM adaptively adjusts scaling factors and weight coefficients, mitigating the curse of dimensionality in multi-objective optimization and enabling real-time parameter tuning. The experimental results also highlight the high quality of the generated sequences: our approach can generate borderline test data that slightly fail to satisfy the target randomness criteria, yet exhibit statistical properties very similar to those of high-entropy sources under standard test suites. These borderline sequences are fault-detectable and provide challenging, realistic test inputs for classical statistical-test-based real-time randomness testers.
Data availability
Not all data is presented in this article; for brevity, only the most relevant results have been included. Additional data or details are available upon reasonable request by contacting the corresponding author.
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Funding
This work is supported by the National Natural Science Foundation of China (No. 62172251).
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P.G. and Z.W conceived the experiment(s), P.G. and B.Z. conducted the experiments, P.G. and C.L. analysed the results. All authors reviewed the manuscript.
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Gao, P., Zhang, B., Wang, Z. et al. Generating borderline test samples for randomness testers via intelligent optimization and evolutionary algorithms. Sci Rep (2026). https://doi.org/10.1038/s41598-026-38020-w
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DOI: https://doi.org/10.1038/s41598-026-38020-w
