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Connected and Autonomous Vehicles (CAVs) enable various capabilities and functionalities like automated driving assistance, navigation and path planning, cruise control, independent decision making, and low-carbon transportation in the real-time environment. However, the increased CAVs usage renders the potential vulnerabilities in the Internet of Vehicles (IoV) environment, making it susceptible to cyberattacks. An Intrusion Detection System (IDS) is a technique to report network assaults by potential Autonomous Vehicles (AVs) without encryption and authorization procedures for internal and external vehicular communications. This paper proposes an Intelligent IDS (IIDS) to enhance intrusion detection and categorize malicious AVs using a modified Convolutional Neural Network (CNN) with hyperparameter optimization approaches for IoV systems. The proposed IIDS framework works in a 5G Vehicle-to-Everything (V2X) environment to effectively broadcast messages about malicious AVs. Thus IIDS aids in preventing collisions and chaos, enhancing safety monitoring in the traffic. The experimental results depict that the proposed IIDS achieves 98% accuracy in detecting attacks.

Industry 5.0 integrates human ability with machines to satisfy the increasing demands of automation. Autonomous Vehicles (AVs) are vital in Industry 5.0 due to their high mobility and intelligent decision-making. Data collected from AVs using Road Side Units (RSUs) aid in enhanced delivery, automated ride-sharing and minimized latency travel. The AVs are reticent to exchange information with other vehicles to ensure data privacy. As there is no trusted environment for data exchange, the AV data are vulnerable to cyber infiltration due to the widespread use of software and the activation of wireless connections. Identifying the source of data that has been shared without authorization is challenging. In this paper, we exploit Machine Learning (ML) with an Intrusion Detection System (IDS) that incorporates Stochastic Gradient Descent (SGD) for detecting intrusions in assistance with blockchain for an enhanced trust evaluation in a 5G-V2X Internet of Vehicles (IoV) environment. A detailed analysis demonstrates that the proposed Blockchain assisted IDS (BIDS) is efficient and secures 98% accuracy compared with the other state-of-the-art solutions.