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

TL;DR

This paper presents a simulation-based dataset for cybersecurity attacks on VANETs in connected and autonomous vehicles, aiding the development of machine learning solutions for anomaly detection and secure communication.

Contribution

It introduces a simulation framework and open dataset modeling malicious attacks on VANETs, facilitating research in cybersecurity for CAVs.

Findings

Demonstrates impact of replay and bogus information attacks

Provides an open dataset for machine learning research

Supports development of anomaly detection algorithms

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.