Improving Security and Energy Optimization Using a Hybrid Secure Deep Learning Approach for IoMT Systems

ABSTRACT In healthcare 5.0, the Internet of Medical Things (IoMT) is driving rapid developments that require energy‐efficient and secure solutions. This paper proposes a novel hybrid approach that integrates Public Key Infrastructure (PKI) with Spiking Neural Networks (SNNs) to enhance secure communication and reduce energy consumption in IoMT‐based healthcare systems. The PKI framework provides secure device authentication and encrypted data transmission, while the biologically inspired SNNs enable low‐power, real‐time anomaly detection. Furthermore, the Zebra Optimization Algorithm (ZOA) further enhances system performance. A simulation study shows that the proposed model outperforms existing protocols from a delay perspective, throughput perspective, packet delivery ratio perspective, and residual energy perspective, showing that it is suitable for real‐time, resource‐constrained healthcare.