Multiscroll hopfield neural network with extreme multistability and its application in video encryption for IIoT

In Industrial Internet of Things (IIoT) production and operation processes, a substantial amount of video data is generated, often containing sensitive personal and commercial information. This paper proposed three new multiscroll Hopfield neural network (MHNN) systems by utilizing an improved segmented nonlinear non-ideal magnetic-controlled memristor model for electromagnetic radiation. Through dynamical methods, the constructed neural network's multidimensional multiscroll attractors and initial offset boosting behavior are analyzed. The observed initial offset boosting behavior demonstrates the system has extreme multistability. Secondly, a video encryption application based on the MHNN system is implemented on the Raspberry Pi platform. This approach encrypts each frame of the extracted video image using a novel encryption algorithm through frame-by-frame encryption, achieving significant encryption results with an information entropy calculation result of 7.9973. This provides strong protection for video data generated in IIoT. Finally, the proposed MHNN system is implemented on Field-Programmable Gate Array (FPGA) digital hardware platform.