Artur Hermann, Natasa Trkulja (Ulm University - Institute of Distributed Systems), Anderson Ramon Ferraz de Lucena, Alexander Kiening (DENSO AUTOMOTIVE Deutschland GmbH), Ana Petrovska (Huawei Technologies), Frank Kargl (Ulm University - Institute of Distributed Systems)

Future vehicles will run safety-critical applications that rely on data from entities within and outside the vehicle. Malicious manipulation of this data can lead to safety incidents. In our work, we propose a Trust Assessment Framework (TAF) that allows a component in a vehicle to assess whether it can trust the provided data. Based on a logic framework called Subjective Logic, the TAF determines a trust opinion for all components involved in processing or forwarding a data item. One particular challenge in this approach is the appropriate quantification of trust. To this end, we propose to derive trust opinions for electronic control units (ECUs) in an in-vehicle network based on the security controls implemented in the ECU, such as secure boot. We apply a Threat Analysis and Risk Assessment (TARA) to assess security controls at design time and use run time information to calculate associated trust opinions. The feasibility of the proposed concept is showcased using an in-vehicle application with two different scenarios. Based on the initial results presented in this paper, we see an indication that a trust assessment based on quantifying security controls represents a reasonable approach to provide trust opinions for a TAF.

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Lightning Community Shout-Outs to:

(1) Jonathan Petit, Secure ML Performance Benchmark (Qualcomm) (2) David Balenson, The Road to Future Automotive Research Datasets: PIVOT Project and Community Workshop (USC Information Sciences Institute) (3) Jeremy Daily, CyberX Challenge Events (Colorado State University) (4) Mert D. Pesé, DETROIT: Data Collection, Translation and Sharing for Rapid Vehicular App Development (Clemson University) (5) Ning…

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Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)