In this paper, a deep learning (DL)-based predictive analysis is proposed to analyze the security of a non-deterministic random number generator (NRNG) using white chaos. In particular, the temporal pattern attention (TPA)-based DL model is employed to learn and analyze the data from both stages of the NRNG: the output data of a chaotic external-cavity semiconductor laser (ECL) and the final output data of the NRNG. For the ECL stage, the results show that the model successfully detects inherent correlations caused by the time-delay signature. After optical heterodyning of two chaotic ECLs and minimal post-processing are introduced, the model detects no patterns among corresponding data. It demonstrates that the NRNG has the strong resistance against the predictive model. Prior to these works, the powerful predictive capability of the model is investigated and demonstrated by applying it to a random number generator (RNG) using linear congruential algorithm. Our research shows that the DL-based predictive model is expected to provide an efficient supplement for evaluating the security and quality of RNGs.

中文翻译：

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使用白混沌对随机数生成器进行基于深度学习的安全验证

在本文中，提出了一种基于深度学习 (DL) 的预测分析，以使用白混沌分析非确定性随机数生成器 (NRNG) 的安全性。特别是，采用基于时间模式注意 (TPA) 的 DL 模型来学习和分析来自 NRNG 两个阶段的数据：混沌外腔半导体激光器 (ECL) 的输出数据和激光器的最终输出数据。无。对于 ECL 阶段，结果表明该模型成功检测到由时延特征引起的固有相关性。在引入两个混沌 ECL 的光学外差和最小后处理后，该模型在相应数据中没有检测到模式。这表明 NRNG 对预测模型具有很强的抵抗力。在这些作品之前，通过使用线性同余算法将该模型应用于随机数生成器 (RNG)，研究并证明了该模型的强大预测能力。我们的研究表明，基于 DL 的预测模型有望为评估 RNG 的安全性和质量提供有效的补充。