Blockchain Assisted Hybrid Intrusion Detection System in Autonomous Vehicles for Industry 5.0
Sudha Anbalagan, Gunasekaran Raja, Sugeerthi Gurumoorthy, Deepak Suresh Rajendran, Kaviyarasu Ayyakannu
Abstract
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.