Secure Hash Algorithm based on Efficient Chaotic Neural Network

Abstract

Secure Hash Algorithm (SHA) is the most popular standard of Cryptographic Hash functions. Several security protocols use SHA to provide message integrity, authentication and digital signature. Nowadays, a new technology based on Chaotic Neural Networks is used to design Hash functions due to the following important properties of Chaos and Neural Networks: non-linearity, compression, confusion and diffusion. Compared to existing Hash functions based on Chaotic Neural Networks, the proposed structure integrates a strong Chaotic generator into neurons instead of using simple Chaotic maps. In fact, simple chaotic maps are not very robust, even against some statistical attacks (Uniformity and NIST). To also reduce the complexity of hash function proposed in ICITST conference (2015), while maintaining strength, we present in this paper a new structure of Hash function. The theoretical analysis and the obtained experimental performances demonstrate the efficiency o f the implemented structure in terms of strong Collision Resistance and High Message Sensitivity compared to SHA-2 and some Chaos-based Hash functions.