Blockchain Cybersecurity Framework for 5G/6G Industrial IoT: Enhancing Industrial and Healthcare Applications With Neural and Graph Networks

ABSTRACT The implementation of 5G and 6G networks has revolutionized the healthcare industry and the Industrial Internet of Things (IIoT) by providing automated procedures, real‐time data processing, and a dependable and high‐quality network. To address these issues, the current manuscript suggests a framework of Blockchain Cybersecurity Framework of 5G/6G Industrial IoT (DHNN–HDGAN–FO), featuring the newly developed cryptographic and AI‐driven security tools. The initial step is a Reversible Automatic Selection Normalization (RRASN) process that is used to perform effective feature selection and noise reduction and ensure interpretability. The data produced by IoT is encrypted with Hybrid Lightweight Cryptography (HLC) with attribute‐based encryption (ABE), which supports low power encryption and fine control of access. By employing a multi‐criteria decision‐making (MCDM)‐based selection process to optimize blockchain consensus selection, the selection guarantees tamper‐proof authentication and resource optimization. A heterogeneous dynamic graph attention network (HDGAN) is used as a hybrid Discrete Hopfield Neural Network (DHNN) and heterogeneous solution to detect attacks in real time. DHNN learns known cyber‐attacks by associative memory and HDGAN is a dynamic network traffic inspection that detects anomalies and new attack patterns. A Flood Optimization Algorithm (FOA) is then applied to optimize the weight parameters to deliver better accuracy, adaptability, and reduce false positives in intrusion detection. The end‐to‐end security model provides scalability, reliability, and privacy‐sensible data transmission with a 99.9% detection rate, 98.7% privacy preservation, and 97.5% false‐positive loss in 5G/6G‐based IIoT and healthcare cybersecurity designs.