The field of IoT healthcare is witnessing significant advancements in security, driven by the need to protect sensitive patient data and prevent cyber threats. Researchers are exploring innovative approaches to secure medical IoT devices and communication networks, including the development of lightweight encryption algorithms, secure key exchange protocols, and robust image encryption methods. These advancements aim to address the vulnerabilities and limitations of existing security solutions, such as the susceptibility to noise and channel fading, and the need for efficient and scalable key management systems. Notably, the integration of chaotic systems, deep learning, and hyperchaotic maps is leading to more secure and efficient image encryption frameworks. Furthermore, the design of secure and lightweight cryptosystems, such as those based on Wildcard Key Derivation Identity-Based Encryption, is enabling scalable trust and secure omnidirectional communication in IoT healthcare services.

Noteworthy papers include: Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings, which proposes a novel approach for secret-key generation using wavelet scattering networks, achieving a 5x improvement in key generation rate compared to traditional benchmarks. TDADL-IE: A Deep Learning-Driven Cryptographic Architecture for Medical Image Security and Elevating Medical Image Security: A Cryptographic Framework Integrating Hyperchaotic Map and GRU, which present robust image encryption frameworks using chaotic systems and deep learning techniques, demonstrating effectiveness against various security threats. SLIE: A Secure and Lightweight Cryptosystem for Data Sharing in IoT Healthcare Services, which introduces a novel cryptosystem based on Wildcard Key Derivation Identity-Based Encryption, ensuring scalable trust and secure omnidirectional communication in IoT healthcare services.