Vehicular Ad hoc NETwork is a promising technology providing important facilities for modern transportation systems. It has garnered much interest from researchers studying the mitigation of attacks including distributed denial of service attacks. Machine learning techniques, which mainly rely on the quality of the datasets used, play a role in detecting many attacks with a high level of accuracy. We conducted a comprehensive literature review and found many limitations on the datasets available for distributed denial of service attacks on Vehicular Ad hoc NETwork including the following: unavailability of online versions, an absence of distributed denial of service traffic, unrepresentative of Vehicular Ad hoc NETwork, and no information regarding the network configurations. Therefore, in this article, we proposed a novel simulation technique to generate a valid dataset called Vehicular Ad hoc NETwork distributed denial of service dataset, which is dedicated to Vehicular Ad hoc NETworks. Vehicular Ad hoc NETwork distributed denial of service dataset holds information on distributed denial of service attack traffic considering Vehicular Ad hoc NETwork architecture, traffic density, attack intensity, and nodes mobility. Well-known simulation tools such as SUMO, OMNeT++, Veins, and INET were used to ensure that all the properties of Vehicular Ad hoc NETwork have been captured. We then compared Vehicular Ad hoc NETwork distributed denial of service dataset with several studies to prove its novelty and evaluated the dataset using several machine learning models. We confirmed that studied models using this dataset achieved high accuracy above 99.5% except support-vector machine that achieved 97.3%.

车载Ad hoc网络是一种有前途的技术，可为现代交通系统提供重要的设施。研究缓解攻击（包括分布式拒绝服务攻击）的研究人员引起了很多兴趣。机器学习技术主要依赖于所使用数据集的质量，在以高准确度检测许多攻击方面发挥着作用。我们进行了全面的文献审查，发现可用于车载Ad hoc网络上的分布式拒绝服务攻击的数据集存在许多局限性，包括以下内容：在线版本不可用，缺少分布式拒绝服务流量，无法代表车载Ad hoc网络，并且没有有关网络配置的信息。因此，在本文中，我们提出了一种新颖的仿真技术来生成一个有效的数据集，称为“车载​​Ad hoc NETwork分布式拒绝服务”数据集，该数据集专门用于车载Ad hoc NETworks。车载Ad hoc NETwork分布式拒绝服务数据集包含有关车载Ad hoc NETwork架构，流量密度，攻击强度和节点移动性的分布式拒绝服务攻击流量的信息。使用诸如SUMO，OMNeT ++，Veins和INET之类的著名仿真工具来确保已捕获车载Ad hoc网络的所有属性。然后，我们将车载Ad hoc网络分布式服务拒绝数据集与多项研究进行了比较，以证明其新颖性，并使用多种机器学习模型对数据集进行了评估。我们确认使用此数据集的研究模型可实现99以上的高精度。