论文标题
模拟对连接和自动驾驶汽车网络安全的恶意攻击：机器学习数据集
Simulating Malicious Attacks on VANETs for Connected and Autonomous Vehicle Cybersecurity: A Machine Learning Dataset
论文作者
论文摘要
连接和自动驾驶汽车（CAVS）依靠车辆和路边基础设施之间的无线通信来支持安全操作。但是，网络安全攻击对Vanets构成威胁和骑士的安全操作。这项研究建议使用仿真来建模可能受到恶意攻击的典型通信场景。 Eclipse Mosaic模拟框架用于模拟两个典型的道路场景，包括车辆和基础设施之间的消息传递 - 引入了重播和虚假信息网络安全攻击。该模型展示了这些攻击的影响，并提供了一个开放的数据集，以告知机器学习算法的开发，以提供异常检测和缓解解决方案，以增强安全的通信和安全在道路上的安全部署。
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.
