A Chaotic Neural Network Based Cryptographic Pseudo-Random Sequence Design

Abstract

Efficient random sequence generators are significant in the application areas of cryptographic stream cipher design, statistical sampling and simulation, direct spread spectrum, etc. A cryptographically efficient pseudo-random sequence should have the characteristics of high randomness and encryption effect. The statistical quality of pseudo-random sequences determines the strength of cryptographic system. The generation of pseudo-random sequences with high randomness and encryption effect is a key challenge. A sequence with poor randomness threatens the security of cryptographic system. In this paper, the features and strengths of chaos and neural network are combined to design a novel pseudo-random binary sequence generator for cryptographic applications. The statistical performance of the proposed chaotic neural network based pseudo random sequence generator is examined against the NIST SP800-22 randomness tests and multimedia image encryption. The results of investigations are promising and depict its relevance for cryptographic applications.