Assessing the quality of Random Number Generators through Neural Networks

ePrint Report: Assessing the quality of Random Number Generators through Neural Networks

José Luis Crespo, Javier González-Villa, Jaime Gutierrez, Angel Valle

In this paper we address the use of Neural Networks (NN) for the
assessment of the quality and hence safety of several Random Number Generators (RNGs), focusing both on the vulnerability of classical Pseudo Random Number Generators (PRNGs), such as Linear Congruential Generators (LCGs) and the RC4 algorithm, and extending our analysis to non-conventional data sources, such as Quantum …

address assessment eprint report networks neural networks quality random random number report safety