Combining Cyber Security Intelligence to Refine Automotive Cyber Threats

Abstract

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 paper, we aim at closing this gap by creating a mapping between UN R155, Microsoft STRIDE classification, Common Attack Pattern Enumerations and Classifications (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.