HNN-HR Chaotic System With Controllable Multistable Memristor for IIoT Image Encryption

With the continuous advancements in computer technology and industrial technology, ensuring the security of industrial information is becoming more crucial. To safeguard against the exposure of sensitive industrial information, research into industrial image encryption technology is crucial. In this paper, a controllable multistable memristor model is presented. The multi-stability of the memristor is analyzed and described by mathematical model. The chaotic system of coupled multistable memristor is constructed using the Hopfield neural network (HNN) and the Hindmarsh-Rose neuron (HR). The intricate dynamic behavior of the HNN-HR chaotic system is uncovered through dynamic analysis and numerical simulations. The equivalent circuit of the HNN-HR chaotic system has been constructed, and the accuracy of the numerical results has been validated. The HNN-HR chaotic system has multi-stability and tunability of initial conditions, which can be used for industrial image encryption. The chaotic sequences generated by the HNN-HR system are utilized for encrypting industrial images by cyclic shift algorithm and bi-directional DNA diffusion algorithm. This paper provides an encryption scheme for the industrial Internet of things (IIoT). The research results indicate that the encryption schemes provide enhanced resistance to attacks. The encryption scheme shows great potential in the field of industrial image encryption and enhances the security of industrial image transmission.