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.

View More Papers

Inaudible Adversarial Perturbation: Manipulating the Recognition of User Speech...

Xinfeng Li (Zhejiang University), Chen Yan (Zhejiang University), Xuancun Lu (Zhejiang University), Zihan Zeng (Zhejiang University), Xiaoyu Ji (Zhejiang University), Wenyuan Xu (Zhejiang University)

Read More

Like, Comment, Get Scammed: Characterizing Comment Scams on Media...

Xigao Li (Stony Brook University), Amir Rahmati (Stony Brook University), Nick Nikiforakis (Stony Brook University)

Read More

Exploring Phishing Threats through QR Codes in Naturalistic Settings

Filipo Sharevski (DePaul University), Mattia Mossano, Maxime Fabian Veit, Gunther Schiefer, Melanie Volkamer (Karlsruhe Institute of Technology)

Read More

Large Language Model guided Protocol Fuzzing

Ruijie Meng (National University of Singapore, Singapore), Martin Mirchev (National University of Singapore), Marcel Böhme (MPI-SP, Germany and Monash University, Australia), Abhik Roychoudhury (National University of Singapore)

Read More