AI-Driven Intrusion Detection in IoV Communication: Insights from CICIoV2024 Dataset

The increasing interconnectivity of vehicular networks through the Internet of Vehicles (IoV) introduces significant security challenges, particularly for the Controller Area Network (CAN), a widely adopted protocol vulnerable to cyberattacks such as spoofing and Denial-of-Service (DoS). To address these challenges, this study explores the potential of Intrusion Detection Systems (IDSs) leveraging artificial intelligence (AI) techniques to detect and mitigate malicious activities in CAN communications. Using the CICIoV2024 dataset, which provides a realistic testbed of vehicular traffic under benign and malicious conditions, we evaluate 25 machine learning (ML) models across multiple metrics, including accuracy, balanced accuracy, F1-score, and computational efficiency. A systematic and repeatable approach was proposed to facilitate testing multiple models and classification scenarios, enabling a comprehensive exploration of the dataset's characteristics and providing insights into various ML algorithms' effectiveness. The findings highlight the strengths and limitations of various algorithms, with ensemble-based and tree-based models demonstrating superior performance in handling imbalanced data and achieving high generalization. This study provides insights into optimizing IDSs for vehicular networks and outlines recommendations for improving the robustness and applicability of security solutions in real-world IoV scenarios.