熵混合网络：通过轻量级动态熵注入增强伪随机数生成器
Entropy Mixing Networks: Enhancing Pseudo-Random Number Generators with Lightweight Dynamic Entropy Injection
AI总结：
针对密码系统等对随机数质量的高要求，本文提出结合确定性伪随机生成与周期性熵注入的新型混合随机数生成器EMN，实验证明其随机质量优于现有常用生成器，更适用于高安全需求的密码场景。
摘要：
随机数生成在密码系统与计算应用中至关重要，均匀性、不可预测性和鲁棒性是其核心要求。本文提出熵混合网络（Entropy Mixing Network，EMN）——一种新型混合随机数生成器，通过结合确定性伪随机生成与周期性熵注入来提升随机数质量。为评估其有效性，我们构建了整合统计测试、高级度量与可视化分析的综合评估框架，可全面衡量随机数质量、可预测性与计算效率。实验结果显示，EMN在关键指标上优于Python的SystemRandom与MersenneTwister，取得最高的卡方p值（0.9430）、熵值（7.9840）与最低的可预测性（-0.0286）。这些提升伴随计算性能的权衡：EMN生成时间更长（0.2602秒）。尽管如此，其优异的随机数质量使其尤其适用于安全性优先于速度的密码学应用场景。
英文摘要：
Random number generation plays a vital role in cryptographic systems and computational applications, where uniformity, unpredictability, and robustness are essential. This paper presents the Entropy Mixing Network (EMN), a novel hybrid random number generator designed to enhance randomness quality by combining deterministic pseudo-random generation with periodic entropy injection. To evaluate its effectiveness, we propose a comprehensive assessment framework that integrates statistical tests, advanced metrics, and visual analyses, providing a holistic view of randomness quality, predictability, and computational efficiency. The results demonstrate that EMN outperforms Python's SystemRandom and MersenneTwister in critical metrics, achieving the highest Chi-squared p-value (0.9430), entropy (7.9840), and lowest predictability (-0.0286). These improvements come with a trade-off in computational performance, as EMN incurs a higher generation time (0.2602 seconds). Despite this, its superior randomness quality makes it particularly suitable for cryptographic applications where security is prioritized over speed.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
