A true random number generator (TRNG) is proposed, harvesting entropy from multicore CPUs to generate non-deterministic outputs. The entropy source is the unpredictable sequence of thread access when parallel threads attempt to access the same memory location, known as race condition or data races. Although prior work using the same entropy source exists, they either have low efficiency or insufficient security analysis. The novelty of this work lies in its use of chaotic networks capable of extracting entropy while postprocessing outputs simultaneously. These networks are formulated by coupling chaotic maps in the form of chaotic coupled map lattices which have the capability to amplify minor uncertainties, leading to better performance as compared to other CPU-based TRNGs. We first perform experiments to depict the unpredictable nature of thread access due to race conditions through entropy and scale index analysis. Next, the proposed generator is scrutinized based on a standardized set of evaluation criteria which includes the use of multiple statistical test suites followed by an analysis of its non-deterministic property. We also perform an in-depth entropy analysis of the generator’s outputs and measure its degree of non-periodicity. Results indicate that the proposed chaos-based TRNG is fast, evenly distributed, and is secure enough for applications that have high security requirements.

提出了一种真正的随机数发生器（TRNG），它从多核CPU收集熵以产生不确定的输出。当并行线程尝试访问相同的内存位置（称为竞争条件或数据竞争）时，熵源是线程访问的不可预测的顺序。尽管存在使用相同熵源的先前工作，但是它们要么效率低下，要么安全性分析不足。这项工作的新颖之处在于它使用了能够在同时对输出进行后处理的同时提取熵的混沌网络。这些网络是通过以混沌耦合映射点阵形式耦合混沌映射表来制定的，与其他基于CPU的TRNG相比，混沌耦合映射点阵具有放大较小不确定性的能力，从而导致更好的性能。我们首先进行实验，以通过熵和尺度索引分析来描述由于竞争条件而导致的线程访问的不可预测性。接下来，将基于标准化的评估标准集对提议的生成器进行审查，其中包括使用多个统计测试套件，然后分析其不确定性。我们还对生成器的输出进行了深入的熵分析，并测量了其非周期性的程度。结果表明，所提出的基于混沌的TRNG快速，均匀地分布，并且对于具有高安全性要求的应用程序足够安全。建议的生成器是根据一组标准化的评估标准进行审查的，评估标准包括使用多个统计测试套件，然后分析其不确定性。我们还对生成器的输出进行了深入的熵分析，并测量了其非周期性的程度。结果表明，所提出的基于混沌的TRNG快速，均匀地分布，并且对于具有高安全性要求的应用程序是足够安全的。建议的生成器是根据一组标准的评估标准进行审查的，评估标准包括使用多个统计测试套件，然后分析其不确定性。我们还对生成器的输出进行了深入的熵分析，并测量了其非周期性的程度。结果表明，所提出的基于混沌的TRNG快速，均匀地分布，并且对于具有高安全性要求的应用程序足够安全。