A Chaotic Tabu Learning Neuron-Based Hybrid Cryptography Solution for CIoMT

With the evolution of mobile communication technologies, consumer medical electronics have emerged as a critical enabler for ubiquitous information exchange across Internet of Medical Things (IoMT). The integration of chaotic encryption and communication offers significant potential to enhance communication security by leveraging highly initial sensitivity and strong randomness of chaotic systems. This paper presents a novel hybrid chaotic encryption scheme and its integration with secure communication in consumer IoMT (CIoMT) systems. We propose a new chaotic system based on a Tabu learning neuron (TLN) and two nonlinear memristors, named chaotic TLN (CTLN), addressing the need for high security performance. The proposed CTLN is modeled by using two nonlinear memristors to describe the influence of external electromagnetic radiation on TLN. Dynamical analyses show that the CTLN has extremely complex chaotic characteristics, including butterfly attractors, multi-single-butterfly attractors, multi-double-butterfly attractors, and coexisting multi-butterfly attractors. Furthermore, in order to explore the feasibility of its hardware implementation, an analog CLTN circuit is designed and experimentally verified. Finally, we introduce a novel secure framework that integrates chaotic neural computing, DNA encoding and Compressed sensing to improve communication security in consumer IoMT. The security analysis reveals that the proposed hybrid cryptography solution exhibits robust resistance to differential attacks and high information entropy, effectively preserving the privacy and unaltered nature of sensitive medical images in the Internet of Medical Things. This paper aims to bridge the gap between information security and communication in CIoMT systems, offering insights into integrating biology-inspired technologies such as chaotic neural networks and DNA for more security consumer applications.