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.
Beijing Microelectronics Technology Institute (BMTI), Beijing, 10076, People's Republic of China.
Sci Rep. 2022 Jul 28;12(1):12983. doi: 10.1038/s41598-022-13286-y.
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.
本文提出了一种基于忆阻神经网络的高级加密标准（AES）密码系统。利用忆阻器的非线性特性，构建了忆阻混沌神经网络。将混沌序列作为 AES 分组的初始密钥，该混沌序列具有初值敏感性和良好的随机性，实现了“一次一密”的动态加密。此外，还应用里维斯特-沙米尔-阿德曼（RSA）算法对忆阻神经网络参数的初始值进行加密。结果表明，与传统 AES 相比，该算法具有更高的安全性、更大的密钥空间和更强的鲁棒性。该算法可以有效抵抗初始密钥固定和穷举攻击。此外，还分析了器件变异性对忆阻神经网络的影响，并提出了一种电路结构。
