| Résumé | Considering the complexity of network traffic in IoV operations, methods that can identify complex patterns become useful. Machine learning fosters several techniques to enhance the detection, prevention, and mitigation of cyberattacks. However, important features are not addressed in the current state-of-the-art security datasets for IoV. For example, in the case of intra-vehicle communications, it is critical to consider the interaction among multiple Electronic Control Units (ECUs). Also, mimicking a realistic IoV environment is not simple since establishing a test environment requires considerable financial investment. Hence, there is a need for a testbed composed of several real ECUs in an IoV environment comprising network traffic. Thereupon, the main goal of this research is to propose a realistic benchmark dataset to foster the development of new cybersecurity solutions for IoV operations. To accomplish this, five attacks were executed against the fully intact inner structure of a 2019 Ford car, complete with all ECUs. However, the vehicle was immobile and incapable of causing any potential harm or injuries. Hence, all attacks were carried out on the vehicle without endangering the car’s driver or passengers. These attacks are classified as spoofing and Denial-of-Service (Dos) and were carried out through the Controller Area Network (CAN) protocol. This effort establishes a baseline complementary to existing contributions and supports researchers in proposing new IoV solutions to strengthen overall security using different techniques (e.g., Machine Learning — ML). The CICIoV2024 dataset has been published on CIC’s dataset page. |
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