PRNG Implementation Based on Chaotic Neural Network (CNN)

Abstract

In this work, a neural network with chaos activation function has been applied as a pseudo-random number generator (PRNG). Chaotic neural network (CNN) is used because of its noise like behaviour which is important for cryptanalyst to know about the hidden information as it is hard to predict. A suitable adaptive architecture was adopted to generate a binary number and the result was tested for randomness using National Institute of Standard Technology (NIST) randomness tests.
 Although the applications of CNN in cryptography have less effective than traditional implementations, this is because these systems need large numbers of digital logic or even a computer system. This work will focus on applications that can use the proposed system in an efficient way that minimize the system complexity.