  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.
