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

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 simulati...

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Bibliographic Details
Published in2022 13th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP) pp. 332 - 337
Main Authors Iqbal, Safras, Ball, Peter, Kamarudin, Muhammad H, Bradley, Andrew
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.07.2022
Subjects
Autonomous Vehicle
Bogus information Attack
CAV
Connected vehicles
Cyber Attack
Intelligent Transport
Machine learning algorithms
Replay Attack
Roads
Training
V2I
V2V
VANET
Vehicle-to-infrastructure
Vehicular ad hoc networks
Wireless communication
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Summary: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 a public 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.
DOI:10.1109/CSNDSP54353.2022.9908023