Stacking Enabled Ensemble Learning Based Intrusion Detection Scheme (SELIDS) for IoV

A revolutionary approach for enhancing driving efficiency and safety in intelligent transportation systems (ITS) is deploying autonomous vehicles. Vehicle-to-everything (V2X) technology facilitates interactions between vehicles and infrastructure. However, the Internet of Vehicles (IoV) is susceptible to many cyberattacks, encompassing impersonation, denial of service (DoS), and fuzzy assaults. This paper proposes an intelligent network intrusion detection system (NIDS) using machine learning algorithms. The usage of the ML approach in a Stacking-enabled Ensemble Learning-based Intrusion Detection Scheme (SELIDS) for IoV is proposed. We additionally examine each technique’s shortcomings and how they affect the NIDS efficiency. Deploying the proposed NIDS on the benchmark dataset demonstrates the capacity of the system to recognize different kinds of assaults. Finally, we explore the potential for NIDS to collaborate with additional security technologies in the future. Empirical results prove the efficacy of the proposed mechanism.