Paper 2024/578

Assessing the quality of Random Number Generators through Neural Networks

Abstract

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 Random Number Generators (QRNGs) based on Vertical-Cavity Surface- Emitting Laser (VCSEL). Among the results found, we identified a sort of classification of generators under different degrees of susceptibility, underlining the fundamental role of design decisions in enhancing the safety of PRNGs. The influence of network architecture design and associated hyper-parameters variations was also explored, highlighting the effectiveness of longer sequence lengths and convolutional neural networks in enhancing the discrimination of PRNGs against other RNGs. Moreover, in the prediction domain, the proposed model is able to deftly distinguish the raw data of our QRNG from truly random ones, exhibiting a cross-entropy error of 0.52 on the test data-set used. All these findings reveal the potential of NNs to enhance the security of RNGs, while highlighting the robustness of certain QRNGs, in particular the VCSEL-based variants, for high-quality random number generation applications.