Pseudorandom number generators based on neural networks: a review

Abstract

Pseudorandom number generators are deterministic algorithms capable of producing sequences of numbers that appear sufficiently ”random,” and they find extensive applications across various domains such as cryptography, network security, communications, machine learning, and gaming. As highly nonlinear mathematical systems, neural networks exhibit characteristics such as fitting ability, unidirectional property, generalization capability, and parallelism, which render them prominent in the design of PRNGs and a topic of significant research interest. To date, there exists no comprehensive review focusing on the utilization of neural networks for the design of PRNGs. This paper categorizes existing neural network-based PRNG design schemes into three types: those based on recurrent neural network models and their variants, such as Long Short-Term Memory (LSTM) models; those based on generative adversarial networks (GANs); and those based on deep reinforcement learning. Subsequently, the paper elucidates the design philosophies and typical algorithmic principles underlying these schemes, compares these algorithms, summarizes the existing challenges, and discusses prospective research directions.