Assessing Security of Internal Vehicle Networks

• 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.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Programvaruteknik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Software Engineering (hsv//eng)

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