Li Cai, Zhang Jianguo, Sang Luxiao, Gong Lishuang, Wang Longsheng, Wang Anbang, Wang Yuncai
Key Laboratory of Advanced Transducers and Intelligent Control System, Ministry of Education, Taiyuan University of Technology, Taiyuan 030024, China.
College of Physics and Optoelectronics, Taiyuan University of Technology, Taiyuan 030024, China.
Entropy (Basel). 2020 Oct 6;22(10):1134. doi: 10.3390/e22101134.
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.
本文提出了一种基于深度学习（DL）的预测分析方法，用于分析使用白混沌的非确定性随机数发生器（NRNG）的安全性。具体而言，采用基于时间模式注意力（TPA）的深度学习模型来学习和分析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NRNG的最终输出数据。对于ECL阶段，结果表明该模型成功检测到了由时延特征引起的内在相关性。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未检测到模式。这表明NRNG对预测模型具有很强的抗性。在这些工作之前，通过将该模型应用于使用线性同余算法的随机数发生器（RNG），研究并证明了该模型强大的预测能力。我们的研究表明，基于深度学习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。
