Florian Sommer

Modern vehicles increasingly rely on electronics, software, and communication technologies (cyber space) to perform their driving task. Over-The-Air (OTA) connectivity further extends the cyber space by creating remote access entry points. Accordingly, the vehicle is exposed to security attacks that are able to impact road safety. A profound understanding of security attacks, vulnerabilities, and mitigations is necessary to protect vehicles against cyber threats. While automotive threat descriptions, such as in UN R155, are still abstract, this creates a risk that potential vulnerabilities are overlooked and the vehicle is not secured against them. So far, there is no common understanding of the relationship of automotive attacks, the concrete vulnerabilities they exploit, and security mechanisms that would protect the system against these attacks. In this article, we aim at closing this gap by creating a mapping between UN R155, Microsoft STRIDE classification, Common Attack Pattern Enumeration and Classification (CAPEC), and Common Weakness Enumeration (CWE). In this way, already existing detailed knowledge of attacks, vulnerabilities, and mitigations is combined and linked to the automotive domain. In practice, this refines the list of UN R155 threats and therefore supports vehicle manufacturers, suppliers, and approval authorities to meet and assess the requirements for vehicle development in terms of cybersecurity. Overall, 204 mappings between UN threats, STRIDE, CAPEC attack patterns, and CWE weaknesses were created. We validated these mappings by applying our Automotive Attack Database (AAD) that consists of 361 real-world attacks on vehicles. Furthermore, 25 additional attack patterns were defined based on automotive-related attacks.

Modern connected or autonomous vehicles (AVs) are highly complex cyber-physical systems. As a result of the high number of different technologies and connectivity features involved, testing these systems to identify security vulnerabilities is a big challenge. Security testing techniques, such as penetration testing, are often manual methods that are applied comparatively late in the vehicle development process. Thus, vulnerabilities are only detected late or after development, leading to higher costs and more patching effort. To reduce the amount of testing resources in general and enable early and automated testing, model-based testing methods have been established in several domains, such as information technology and the automotive domain. The transfer of model-based testing approaches to automotive security testing could help to detect vulnerabilities earlier than other, manual methods by automatically generating, executing, or simulating security tests. In this study, we review the literature on model-based test approaches in the automotive domain. First, we consider security-independent approaches to obtain an overview of applied models, formalisms, test selection criteria, and test generation techniques. In addition, we investigate, whether and how model-based approaches are applied for automotive security testing. Overall, we identified 63 publications related to model-based testing and 29 publications with regard to model-based security testing. The aim of this study is to provide an overview and direct comparison between these approaches. In this manner, the state of model-based security testing in the automotive domain, current challenges, and potential research areas are determined.

Due to current development trends in the automotive industry towards stronger connected and autonomous driving, the attack surface of vehicles is growing which increases the risk of security attacks. This has been confirmed by several research projects in which vehicles were attacked in order to trigger various functions. In some cases these functions were critical to operational safety. To make automotive systems more secure, concepts must be developed that take existing attacks into account. Several taxonomies were proposed to analyze and classify security attacks. However, in this paper we show that the existing taxonomies were not designed for application in the automotive development process and therefore do not provide enough degree of detail for supporting development phases such as threat analysis or security testing. In order to be able to use the information that security attacks can provide for the development of security concepts and for testing automotive systems, we propose a comprehensive taxonomy with degrees of detail which addresses these tasks. In particular, our proposed taxonomy is designed in such a wa, that each step in the vehicle development process can leverage it.