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<p>Random Number Generators (RNGs) adopt algorithm devised to generate a sequence of numbers devoid of any detectable predictability in their pattern. These random number generators have diverse applications in cryptography, statistical sampling, simulations and security. These are primarily used for operations like generating one-time passwords. RNGs are used because they increase unpredictability, efficiency and versatility. This paper presents a comprehensive survey and comparison of diverse RNGs through various parameters, including entropy and correlation coefficient evaluation. Consequently, analysis based on these metrics and histogram analysis are demonstrated in this document. Innumerable logistic, chaotic and other maps are available to generate these random numbers. Each RNG’s statistical quality and randomness properties were evaluated using Python, and the random number generators were meticulously assessed.</p>

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Logistic Map-Based Deterministic Random Number Generators: A Survey

  • 期刊论文

  • 发表日期：

    2026年3月9日

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  • Nishigandha Mittal,
  • Sangeeta Dhall,
  • Lalit Rai,
  • Shailender Gupta

摘要

Random Number Generators (RNGs) adopt algorithm devised to generate a sequence of numbers devoid of any detectable predictability in their pattern. These random number generators have diverse applications in cryptography, statistical sampling, simulations and security. These are primarily used for operations like generating one-time passwords. RNGs are used because they increase unpredictability, efficiency and versatility. This paper presents a comprehensive survey and comparison of diverse RNGs through various parameters, including entropy and correlation coefficient evaluation. Consequently, analysis based on these metrics and histogram analysis are demonstrated in this document. Innumerable logistic, chaotic and other maps are available to generate these random numbers. Each RNG’s statistical quality and randomness properties were evaluated using Python, and the random number generators were meticulously assessed.