A Web 3.0 Integrated Blockchain Enabled Access System Augmented by Meta-Heuristic Cognitive Learning Framework for Mitigating Threats in IoT Enabled Consumer Electronic Devices

Consumer Electronic Devices have become an open network model because of the infusion of the Internet of Things
(IoT) and other communication technologies such as 5G/6G.
Though these devices have provided the high-end sophistication even to common person, but it has proved its darker side by triggering more security breaches and privacy problems. Hence, securing and authenticating these Internet enabled consumer devices has become a probable issue to be solved for safer and secured communication. Therefore, this paper presents a novel fusion of Web 3.0- based Blockchain (WBC) and Deep learning (DL) technique for securing consumer electronic devices in an IoT ecosystem. The proposed framework k(MTD-BCAM) is devised into two components: Multiple-Threat Detection(MTD) and Access Management Mechanism(AMM). In the first component, a DL model is applied for threat detection, whereas WBC is meant for an efficient authentication process. Furthermore, a novel
residual fast-gated recurrent neural network is proposed. To
reduce the complexity, the komodo Mlipir optimization (KMO)