用于最小熵估计的机器学习预测器
Machine Learning Predictors for Min-Entropy Estimation
AI总结：
本文研究机器学习预测器在随机数生成器最小熵估计中的应用，发现此类预测器主要估计平均最小熵，并证明CNN-LSTM混合模型和GPT-2在某些场景下优于NIST SP 800-90B预测器。
摘要：
本研究探讨了机器学习预测器在随机数生成器（RNG）最小熵估计中的应用，RNG是密码学应用中的关键组成部分，准确的熵评估对网络安全至关重要。我们的研究表明，这些预测器，以及任何利用序列相关性的预测器，主要估计的是平均最小熵，而这一度量在该背景下尚未得到广泛研究。我们探索了平均最小熵与传统最小熵之间的关系，重点关注它们对被预测目标比特数量的依赖性。利用来自广义二元自回归模型（马尔可夫过程的一个子集）的数据，我们证明机器学习模型（包括卷积与循环长短期记忆层的混合模型以及基于Transformer的GPT-2模型）在某些场景下优于传统的NIST SP 800-90B预测器。我们的发现强调了在RNG最小熵评估中考虑目标比特数量的重要性，并突显了机器学习方法在增强熵估计技术以提升密码学安全性方面的潜力。
英文摘要：
This study investigates the application of machine learning predictors for min-entropy estimation in Random Number Generators (RNGs), a key component in cryptographic applications where accurate entropy assessment is essential for cybersecurity. Our research indicates that these predictors, and indeed any predictor that leverages sequence correlations, primarily estimate average min-entropy, a metric not extensively studied in this context. We explore the relationship between average min-entropy and the traditional min-entropy, focusing on their dependence on the number of target bits being predicted. Utilizing data from Generalized Binary Autoregressive Models, a subset of Markov processes, we demonstrate that machine learning models (including a hybrid of convolutional and recurrent Long Short-Term Memory layers and the transformer-based GPT-2 model) outperform traditional NIST SP 800-90B predictors in certain scenarios. Our findings underscore the importance of considering the number of target bits in min-entropy assessment for RNGs and highlight the potential of machine learning approaches in enhancing entropy estimation techniques for improved cryptographic security.
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