Combining Cyber Security Intelligence to Refine Automotive Cyber Threats

被引:1
|
作者
Sommer, Florian [1 ]
Gierl, Mona [1 ]
Kriesten, Reiner [1 ]
Kargl, Frank [2 ]
Sax, Eric [3 ]
机构
[1] Karlsruhe Univ Appl Sci, Moltkestr 30, D-76133 Karlsruhe, Baden Wurttembe, Germany
[2] Ulm Univ, Helmholtzstr 16, D-89081 Ulm, Baden Wurttembe, Germany
[3] Karlsruhe Inst Technol, Kaiserstr 12, D-76131 Karlsruhe, Baden Wurttembe, Germany
关键词
Automotive security; taxonomies; attacks; vulnerabilities; mitigations;
D O I
10.1145/3644075
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
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.
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页数:34
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