Summary
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 re
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