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

ABSTRACT: 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.
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.
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset
http://arxiv.org/abs/2202.07704v1