Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset
This work addresses cybersecurity threats to CAV safety by providing a dataset for developing detection algorithms, but it is incremental as it builds on existing simulation methods.
The study simulated malicious attacks on Vehicular Adhoc Networks (VANETs) for Connected and Autonomous Vehicles (CAVs) using the Eclipse MOSAIC framework, creating an open dataset to support machine learning-based anomaly detection and mitigation solutions.
Connected and Autonomous Vehicles (CAVs) rely on Vehicular Adhoc Networks with wireless communication between vehicles and roadside infrastructure to support safe operation. However, cybersecurity attacks pose a threat to VANETs and the safe operation of CAVs. This study proposes the use of simulation for modelling typical communication scenarios which may be subject to malicious attacks. The Eclipse MOSAIC simulation framework is used to model two typical road scenarios, including messaging between the vehicles and infrastructure - and both replay and bogus information cybersecurity attacks are introduced. The model demonstrates the impact of these attacks, and provides an open dataset to inform the development of machine learning algorithms to provide anomaly detection and mitigation solutions for enhancing secure communications and safe deployment of CAVs on the road.