A dynamic AES cryptosystem based on memristive neural network.

Journal: Scientific reports
Published Date: Jul 28, 2022

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.

Authors

  • Y A Liu
    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.
  • L Chen
    College of Computing, Georgia Institute of Technology, Atlanta, GA, USA.
  • X W Li
    Beijing Microelectronics Technology Institute (BMTI), Beijing, 10076, People's Republic of China.
  • Y L Liu
    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.
  • S G Hu
    State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • Q Yu
    State Key Laboratory of Electronic Thin Films and Integrated Devices, University of Electronic Science and Technology of China, Chengdu 610054, China.
  • T P Chen
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798, Singapore.
  • Y Liu
    Google Health Palo Alto California USA.

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