Assessing Security of Internal Vehicle Networks
Paper i proceeding, 2023

Automotive software grows exponentially in size. In premium vehicles, the size can reach over 100 million lines of code. One of the challenges in such a large software is how it is architecturally designed and whether this design leads to security vulnerabilities. In this paper, we report on a design science research study aimed at understanding the vulnerabilities of modern premium vehicles. We used machine learning to identify and reconstruct signals within the vehicle's communication networks. The results show that the distributed software architectures can have security vulnerabilities due to the high connectivity of modern vehicles; and that the security needs to be seen holistically - both when constructing the vehicle's software and when designing communication channels with cloud services. The paper proposed a number of measures that can help to address the identified vulnerabilities.

Författare

Anas Alkoutli

Göteborgs universitet

Joakim Anderlind

Göteborgs universitet

Carl-Johan Björnson

Chalmers, Elektroteknik, Elkraftteknik

Göteborgs universitet

Mathias Drage

Göteborgs universitet

Morgan Thowsen

Göteborgs universitet

Chalmers, Data- och informationsteknik, Funktionell programmering

Antonia Welzel

Software Engineering 1

Göteborgs universitet

Miroslaw Staron

Göteborgs universitet

Chalmers, Data- och informationsteknik, Software Engineering

Lecture Notes in Computer Science

0302-9743 (ISSN) 16113349 (eISSN)

Vol. 13928 151-164
978-3-031-36888-2 (ISBN)

16th European Conference on Software Architecture (ECSA)
Prague, Czech Republic,

Ämneskategorier

Programvaruteknik

Datavetenskap (datalogi)

DOI

10.1007/978-3-031-36889-9_12

Mer information

Senast uppdaterat

2024-10-29