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From TARA to Test: Automated Automotive Cybersecurity Test Generation Out of Threat Modeling
Mälardalen University, School of Innovation, Design and Engineering, Embedded Systems. Avl List GmbH, Graz, Austria.ORCID iD: 0000-0001-8556-1541
Ait Austrian Institute of Technology GmbH, Vienna, Austria.
Ait Austrian Institute of Technology GmbH, Vienna, Austria.
Ait Austrian Institute of Technology GmbH, Vienna, Austria.
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2023 (English)In: Proceedings: CSCS 2023 - 7th ACM Computer Science in Cars Symposium, Association for Computing Machinery, Inc , 2023Conference paper, Published paper (Refereed)
Abstract [en]

The United Nations Economic Commission for Europe (UNECE) demands the management of cyber security risks in vehicle design and that the effectiveness of these measures is verified by testing. Generally, with rising complexity and openness of systems via software-defined vehicles, verification through testing becomes a very important for security assurance. This mandates the introduction of industrial-grade cybersecurity testing in automotive development processes. Currently, the automotive cybersecurity testing procedures are not specified or automated enough to be able to deliver tests in the amount and thoroughness needed to keep up with that regulation, let alone doing so in a cost-efficient manner. This paper presents a methodology to automatically generate technology-agnostic test scenarios from the results of threat analysis and risk assessment (TARA) process. Our approach is to transfer the resulting threat models into attack trees and label their edges using actions from a domain-specific language (DSL) for attack descriptions. This results in a labelled transitions system (LTS), in which every labelled path intrinsically forms a test scenario. In addition, we include the concept of Cybersecurity Assurance Levels (CALs) and Targeted Attack Feasibility (TAF) into testing by assigning them as costs to the attack path. This abstract test scenario can be compiled into a concrete test case by augmenting it with implementation details. Therefore, the efficacy of the measures taken because of the TARA can be verified and documented. As TARA is a de-facto mandatory step in the UNECE regulation and the relevant ISO standard, automatic test generation (also mandatory) out of it could mean a significant improvement in efficiency, as two steps could be done at once.

Place, publisher, year, edition, pages
Association for Computing Machinery, Inc , 2023.
Keywords [en]
Automotive, CAL, Cybersecurity, Life Cycle, TAF, Testing
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:mdh:diva-65679DOI: 10.1145/3631204.3631864ISI: 001150368200005Scopus ID: 2-s2.0-85182016784ISBN: 9798400704543 (print)OAI: oai:DiVA.org:mdh-65679DiVA, id: diva2:1830922
Conference
7th ACM Computer Science in Cars Symposium, CSCS 2023, Darmstadt, 5 December 2023
Available from: 2024-01-24 Created: 2024-01-24 Last updated: 2024-03-01Bibliographically approved
In thesis
1. Model-Driven Security Test Case Generation Using Threat Modeling and Automata Learning
Open this publication in new window or tab >>Model-Driven Security Test Case Generation Using Threat Modeling and Automata Learning
2024 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

Automotive systems are not only becoming more open through developments like advanced driving assistance functions, autonomous driving, vehicle-to-everything communication and software-defined vehicle functionality, but also more complex. At the same time, technology from standard IT systems become frequently adopted in this setting. These developments have two negative effects on correctness and security: the rising complexity adds potential flaws and vulnerabilities while the increased openness expands attack surfaces and entry points for adversaries. To provide more secure systems, the amount of verifying system security through testing has to be significantly increased, which is also a requirement by international regulation and standards. Due to long supply chains and non-disclosure policies, verification methods often have to operate in a black box setting. This thesis strives therefore towards finding more efficient methods of automating test case generation in both white and black box scenarios. The focus lies on communication protocols used in vehicular systems. The main approaches used are model-based methods. We provide a practical method to automatically obtain behavioral models in the form of state machines of communication protocol implementations in real-world settings using automata learning. We also provide a means to automatically check these implementation models for their compliance with a specification (e.g., from a standard). We furthermore present a technique to automatically derive test-cases to point out found deviations on the actual system.We also present a method to create abstract cybersecurity test case specifications from semi-formal threat models using attack trees. 

Place, publisher, year, edition, pages
Västerås: Mälardalen University, 2024
Series
Mälardalen University Press Licentiate Theses, ISSN 1651-9256 ; 355
National Category
Computer and Information Sciences Computer Sciences
Research subject
Computer Science
Identifiers
urn:nbn:se:mdh:diva-66165 (URN)978-91-7485-638-5 (ISBN)
Presentation
2024-04-25, U2-024 och via Teams, Mälardalens universitet, Västerås, 10:00 (English)
Opponent
Supervisors
Available from: 2024-03-04 Created: 2024-03-01 Last updated: 2024-04-04Bibliographically approved

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Marksteiner, StefanSjödin, MikaelSirjani, Marjan

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