Enhancing Cybersecurity in Internet of Vehicles: A Machine Learning Approach with Explainable AI for Real-Time Threat Detection

The proliferation of IoV technologies has revolutionized the use of transport systems to a great level of improvement in safety and efficiency, and convenience to users. On the other hand, increased connectivity has also brought new vulnerabilities, making IoV networks susceptible to a wide range of cyber-attacks. The contribution of this paper is the in-depth study of the development and evaluation of advanced machine learning (ML) models that detect and classify network anomalies in IoV ecosystems. Several classification models have been studied in our research to achieve high accuracy for discriminating between benign and malicious traffic. This work further harnesses Explainable AI (XAI) methodologies through the LIME framework for enhanced interpretability of models' decision-making processes. Experimental results strongly advocate the strength of Random Forest and XGBoost, proving to be better on the binary and multi-class classification tasks, respectively. Due to resilience, preciseness, and scalability these models are a practical choice in real-world IoV security frameworks. Explainability integrated not only strengthens model reliability but also closes the gap between performance and interoperability in vehicular networks.