Low-complexity Machine Learning Architecture for Hardware-aware True Random Number Generators Assessment and Continuous Monitoring

A new machine learning approach of assessment and monitoring of True Random Number Generators (TRNGs) is explored in order to create a new technical framework for entropy source development and validation, based on the direct estimation of the core source entropy, which is suitable for hardware-based lightweight cryptography. It consists of the integration between a pre-processing stage and an Artificial Neural Network (ANN). The pre-processing stage continuously generates stochastically patterned images from number sequences provided by TRNGs with assigned entropy thresholds. The ANN processes the images to determine whether the TRNG entropy remains higher than the assigned threshold, dynamically, during the sequence generation. A custom dataset, generated from a Markovian TRNG has been used to train, validate and test a very compact 3-layer ANN. The model achieves accuracy, precision, recall, and F-score all equal to 98.28% averaged on the test set. The low computational complexity of the ANN, favored by the effectiveness of the pre-processing stage, which, in turn, requires a simple architecture to be implemented, proves that the proposed solution can be effectively employed for a perspective resource-constrained hardware implementation.