A Dynamic AES Encryption Based on Memristive Chaos Neural Network

Abstract

This paper proposes an Advanced Encryption Standard (AES) encryption technique based on memristive neural network. A memristive chaotic neural network is constructed by the use of the nonlinear characteristics of the memristor. The chaotic sequence, which is sensitive to the initial value and has good random characteristics, is used as the initial key of AES grouping to realize "one-time-one-secret" dynamic encryption. Results show that the algorithm has higher security, larger key space and stronger robustness than the conventional AES. It can effectively resist the initial key fixed and exhaustive attacks.