A dynamic AES cryptosystem based on memristive neural network

Y A Liu; L Chen; X W Li; Y L Liu; S G Hu; Q Yu; T P Chen; Y Liu

doi:10.1038/s41598-022-13286-y

A dynamic AES cryptosystem based on memristive neural network

Sci Rep. 2022 Jul 28;12(1):12983. doi: 10.1038/s41598-022-13286-y.

Authors

Y A Liu¹, L Chen², X W Li², Y L Liu¹, S G Hu¹, Q Yu¹, T P Chen³, Y Liu⁴

Affiliations

¹ State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China.
² Beijing Microelectronics Technology Institute (BMTI), Beijing, 10076, People's Republic of China.
³ Nanyang Technological University, Singapore, 639798, Singapore.
⁴ State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu, 610054, People's Republic of China. yliu1975@uestc.edu.cn.

Abstract

This paper proposes an advanced encryption standard (AES) cryptosystem based on memristive neural network. A memristive chaotic neural network is constructed by using the nonlinear characteristics of a memristor. A chaotic sequence, which is sensitive to initial values and has good random characteristics, is used as the initial key of AES grouping to realize "one-time-one-secret" dynamic encryption. In addition, the Rivest-Shamir-Adleman (RSA) algorithm is applied to encrypt the initial values of the parameters of the memristive neural network. The results show that the proposed algorithm has higher security, a larger key space and stronger robustness than conventional AES. The proposed algorithm can effectively resist initial key-fixed and exhaustive attacks. Furthermore, the impact of device variability on the memristive neural network is analyzed, and a circuit architecture is proposed.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Algorithms
Computer Security*
Data Collection
Neural Networks, Computer*