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Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networkswith wireless communication between vehicles and roadside infrastructure tosupport safe operation. However, cybersecurity attacks pose a threat to VANETsand the safe operation of CAVs. This study proposes the use of simulation formodelling typical communication scenarios which may be subject to maliciousattacks. The Eclipse MOSAIC simulation framework is used to model two typicalroad scenarios, including messaging between the vehicles and infrastructure -and both replay and bogus information cybersecurity attacks are introduced. Themodel demonstrates the impact of these attacks, and provides an open dataset toinform the development of machine learning algorithms to provide anomalydetection and mitigation solutions for enhancing secure communications and safedeployment of CAVs on the road.

Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset