A new methodology for analyzing vehicle network topologies for critical hacking

Abstract

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.

Analysis of Security Vulnerability Levels of In-Vehicle Network Topologies Applying Graph Representations

“…On the other hand, the 2018 Toyota Avensis has the highest protection score, accordingly, it appears to be the least vulnerable one. Compared to other research studies [5,17,18] that have in some way ranked vulnerability levels of vehicles on the basis of in-vehicle network topology/architecture, it can be seen that similar results were obtained. A well-known remote exploitation experiment [18] has proven how vulnerable the Jeep Cherokee is, and our analytical calculation gave the same result.…”

“…The results of our previous research pointed out that relatively few security levels can be adequate for efficiently characterizing the in-vehicle network's protection level. Based on the performed regression and cluster analyses, three levels are sufficient to rank the vulnerability of the vehicular communication networks [5]. In accordance with this, the typical IVN architecture frequently consists of three or four hierarchy levels: network domains separated by gateways (first level), subdomains handled by domain controllers (second level), and the bottom of the hierarchy tree is mainly built up from subnetworks (third level) [1].…”

“…In the first step of the method, the relevant characteristics of the IVN topologies (the number of safety-critical ECUs protected, etc.) were determined [5]. Homogeneous vehicle groups were identified by cluster analysis of the relevant characteristics.…”

Cyber Attacks Visualization and Prediction in Complex Multi-Stage Network

“…The methods presented in this research mainly focused on creating the adjacency matrix from the attack graphs (Ghadi et al, 2020). A similar author also gave a robust report on vulnerability assessment using attack graphs.…”

Security Analysis and Improvement of Vehicle Ethernet SOME/IP Protocol

“…At this stage, the connection between cars has become more convenient and fast, and the relationship between vehicles and other external facilities is becoming closer [2]. While this brings more efficiency and speed to people, the security issues of the Internet of Vehicles are also becoming increasingly prominent [3]. Smart cars' systems and terminal devices are vulnerable to attacks, and the service applications and platforms that transmit information between vehicles face different security threats [4].…”