The electronic control units in vehicles communicate with each other using different protocols, forming a complex automotive electronics network. With the increasing connectivity of vehicles to the internet through mobile networks, the vulnerability of these networks has become a growing concern. However, there is currently no widely accepted methodology for assessing vulnerabilities in vehicle electronic systems.
The aim of the thesis is to develop a methodology for vulnerability analysis of networks in vehicles, with the possibility of using the MulVAL attack graph generator application as one of several implementation options.
To address this issue, the student should review existing literature on vulnerability analysis of traditional IP-based networks, which involves identifying all possible attack paths, and apply this approach to the automotive electronics network. The research will involve identifying data sources and collection methods required for vulnerability analysis, generating an attack graph using MulVAL or other suitable applications, and evaluating the results.