Biocomputing and Developmental Systems Group, Lero, The Science Foundation Ireland Research Centre for Software, Computer Science and Information System Department, University of Limerick, Limerick, V94 T9PX, Ireland, UK.
Intel Research and Development Ireland Limited, Leixlip, W23 CX68, Ireland, UK.
Sci Rep. 2022 May 21;12(1):8602. doi: 10.1038/s41598-022-11613-x.
This work investigates the potential for using Grammatical Evolution (GE) to generate an initial seed for the construction of a pseudo-random number generator (PRNG) and cryptographically secure (CS) PRNG. We demonstrate the suitability of GE as an entropy source and show that the initial seeds exhibit an average entropy value of 7.940560934 for 8-bit entropy, which is close to the ideal value of 8. We then construct two random number generators, GE-PRNG and GE-CSPRNG, both of which employ these initial seeds. We use Monte Carlo simulations to establish the efficacy of the GE-PRNG using an experimental setup designed to estimate the value for pi, in which 100,000,000 random numbers were generated by our system. This returned the value of pi of 3.146564000, which is precise up to six decimal digits for the actual value of pi. We propose a new approach called control_flow_incrementor to generate cryptographically secure random numbers. The random numbers generated with CSPRNG meet the prescribed National Institute of Standards and Technology SP800-22 and the Diehard statistical test requirements. We also present a computational performance analysis of GE-CSPRNG demonstrating its potential to be used in industrial applications.
本工作研究了使用语法进化（GE）生成伪随机数生成器（PRNG）和密码安全（CS）PRNG 的初始种子的可能性。我们证明了 GE 作为熵源的适用性，并表明初始种子的平均熵值为 7.940560934，对于 8 位熵，接近 8 的理想值。然后，我们构建了两个随机数生成器，GE-PRNG 和 GE-CSPRNG，它们都使用这些初始种子。我们使用蒙特卡罗模拟来建立 GE-PRNG 的功效，使用设计用于估计 pi 值的实验设置，我们的系统生成了 100,000,000 个随机数。这返回了 pi 的值为 3.146564000，对于实际的 pi 值，精确到六位小数。我们提出了一种称为控制流增量器的新方法来生成密码安全的随机数。CSPRNG 生成的随机数符合规定的国家标准与技术研究所 SP800-22 和 Diehard 统计测试要求。我们还对 GE-CSPRNG 的计算性能进行了分析，展示了其在工业应用中的潜力。
