Monitoring Hardware True Random Number Generators with Artificial Neural Networks: Problem Modeling and Training Dataset Generation

We investigate the possible application of Artificial Neural Networks (ANNs) to monitor and test hardware True Random Number Generators (TRNGs). The preliminary results have been obtained developing original investigation and design tools, including the characterization of optimized statistical estimators, considering a class of TRNGs based on binary Markov Chains. Due to the large amount of data required during the design of the ANN we developed an original flexible Database Management Systems to manage and generate training and testing data samples organized in a Object Oriented Database.