A Secure Authentication and Task Offloading Model Using Blockchain‐Assisted Hybrid Serial Learning in Multiaccess Edge Computing for Vehicular Ad Hoc Networks Sector

ABSTRACT The intelligent transportation system (ITS) is enabled by the vehicular ad hoc networks (VANETs), but the security threats, such as node impersonation, node tampering, and eavesdropping, are the greatest challenges and cause security concerns within the system. The large‐scale vehicular environment is not effectively handled by the previous static and centralized security approaches, which can greatly increase the latency and data integrity problems within the network. Thus, this research proposes a deep learning–assisted blockchain approach for enabling the decentralized, reliable, and secure communication in the VANET. The main contribution of the research is to perform secure authentication and task offloading to enable secure task offloading within the VANET and to guarantee communication performance with minimum energy consumption and delays. First, the data confidentiality, privacy of the task offloading, authentication, and integrity are achieved by introducing blockchain technology. Second, the node authentication is performed using adaptive and attention‐based hybrid serial learning (AAHSL), which is developed with the combination of a deep belief network (DBN) and temporal convolution network (TCN). After authenticating the data within the nodes, the adaptive deep reinforcement learning (ADRL)–based task offloading is proposed for reducing the task completion time within the network. In both models, the parameters are tuned using the pattern improvement parameter–based poor and rich optimization algorithm (PIP‐PROA). The experimental results demonstrate that the proposed approach achieves an FNR of about 3.46% during the authentication process, and the reward score achieved by the designed model during the task offloading process is 9.22. Thus, the effectiveness of the suggested model is confirmed by the experimental analysis.