Hopfield neural networks as pseudo random number generators

Abstract

Pseudo random number generators (PRNG) play a key role in various security and cryptographic applications where the performance of these applications is directly related to the quality of generated random numbers. The design of such random number generators is a challenging task. In this paper, we propose an application of Hopfield Neural Networks (HNN) as pseudo random number generator. This is done based on a unique property of HNN, i.e., its unpredictable behavior under certain conditions. We compare the main features of ideal random number generators with those of PRNG based on Hopfield Neural Networks. We use a battery of statistical tests developed by National Institute of Standards and Technology (NIST) to measure the performance, and to evaluate the quality of the proposed Hopfield random number generator.