This study aims to provide a new approach for describing and measuring the vulnerability of in-vehicle networks regarding cyberattacks. Cyberattacks targeting in-vehicle networks can result in a reasonable threat considering passenger safety. Unlike previous literature, the methodology focuses on a comparatively large sample of vehicle networks (114 objects) by proposing a new framework of statistical techniques for measuring, classifying, and modelling in-vehicle networks concerning the changed vulnerability, instead of dealing with each vehicle network individually. To facilitate understanding of the vulnerability patterns of in-vehicle networks, the dataset has been evaluated through three analytic stages: vulnerability identification, classification, and modeling. The result has helped in ranking vehicles based on their network vulnerability level. The result of the modeling has shown that every additional remote endpoint installation causes a relevant weakening in security. Higher cost vehicles have also appeared to be more vulnerable to cyberattacks, while the increase in the number of segmented network domains has had a positive effect on network security.

这项研究旨在提供一种描述和评估车载网络中有关网络攻击的脆弱性的新方法。考虑到乘客安全，针对车载网络的网络攻击可能会导致合理的威胁。与以前的文献不同，该方法通过提出一种新的统计技术框架来测量，分类和建模与已更改的脆弱性有关的车载网络，而不是针对每个车载网络，而是着眼于较大的车辆网络样本（114个对象）个别地。为了便于理解车载网络的漏洞模式，已通过三个分析阶段评估了数据集：漏洞识别，分类和建模。结果有助于根据车辆的网络漏洞级别对车辆进行排名。建模的结果表明，每增加一个远程端点安装，都会导致安全性的相应削弱。成本较高的车辆似乎也更容易受到网络攻击，而分段网络域数量的增加对网络安全产生了积极影响。