Assessing Security of Internal Vehicle Networks
Paper in 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.
Author
Anas Alkoutli
University of Gothenburg
Joakim Anderlind
University of Gothenburg
Carl-Johan Björnson
Chalmers, Electrical Engineering, Electric Power Engineering
University of Gothenburg
Mathias Drage
University of Gothenburg
Morgan Thowsen
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Functional Programming
Antonia Welzel
Software Engineering 1
University of Gothenburg
Miroslaw Staron
University of Gothenburg
Chalmers, Computer Science and Engineering (Chalmers), Software Engineering (Chalmers)
Lecture Notes in Computer Science
0302-9743 (ISSN) 16113349 (eISSN)
Vol. 13928 151-164978-3-031-36888-2 (ISBN)
16th European Conference on Software Architecture (ECSA)
Prague, Czech Republic,
Prague, Czech Republic,
Subject Categories
Software Engineering
Computer Science
DOI
10.1007/978-3-031-36889-9_12