Robust Enhanced Chaos in Memristive Multiattractor Rulkov Neuron Model With Application to Hardware Image Cryptosystem

Due to their sensitivity to initial conditions and inherent unpredictability, chaotic systems have extensive applications in the domain of Internet of Things (IoT) information encryption. This paper presents a novel memristive multi-attractor Rulkov neuron model (MMRNM) that exhibits enhanced robust chaos, building upon the classical Rulkov neuron model. By modulating the internal parameters of the memristor, the number of multi-ribbon chaotic attractors can be flexibly controlled. Furthermore, homogeneous multistability, characterized by the coexistence of an arbitrary number of homogeneous single-ribbon attractors, is observed in the MMRNM as a result of memristor initial offset boosting. Analysis of amplitude control dynamics indicates that adjusting model parameters can lead to unbounded amplitude modulation. Notably, robust chaos enhancement, achieved through precise tuning of memristor parameters, significantly increases the complexity of chaos in the MMRNM. These findings are validated using an STM32 microcontroller. To explore practical applications, the MMRNM with robust enhanced chaotic sequences is integrated into an improved SIMON algorithm to design a new hardware encryption scheme. Performance evaluations demonstrate that this encryption scheme offers superior efficiency and robust security.