In-vehicle trust assessment framework

  • Today’s vehicles run various safety-critical applications requiring data input from diverse in-vehicle components. Adaptive Cruise Control (ACC), for example, can rely on the data input from components such as lidar, radar, GNSS, and cameras. Malicious manipulation of any of this data compromises the data integrity and can result in safety incidents or accidents on the road. Security mechanisms like intrusion detection can be in place; however, they can not reliably assess the consequences of attacks on a system level or for arbitrary subsystems. In this paper, we present a Trust Assessment Framework (TAF) that allows an in-vehicle application in a complex System-of-Systems to assess whether it can trust the integrity of its input data.The TAF assesses the trustworthiness of every component in the data flow chain based on collected evidence. We explain this concept with the example of ACC and show case two ossible implementations of the TAF inside a vehicle.

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Metadaten
Author:Nataša TrkuljaORCiDGND, Artur HermannORCiDGND, Ana PetrovskaORCiDGND, Alexander KieningORCiDGND, Anderson Ramon Ferraz de LucenaORCiDGND, Frank KarglORCiDGND
URN:urn:nbn:de:hbz:294-103845
DOI:https://doi.org/10.13154/294-10384
Parent Title (English):21th escar Europe : The World's Leading Automotive Cyber Security Conference (Hamburg, 15. - 16.11.2023)
Document Type:Part of a Book
Language:English
Date of Publication (online):2023/10/25
Date of first Publication:2023/10/25
Publishing Institution:Ruhr-Universität Bochum, Universitätsbibliothek
Tag:In-vehicle Security; Trust Assessment Framework; Trust Models
Pagenumber:15
Dewey Decimal Classification:Allgemeines, Informatik, Informationswissenschaft / Informatik
open_access (DINI-Set):open_access
Konferenz-/Sammelbände:21th escar Europe : The World's Leading Automotive Cyber Security Conference
Licence (German):License LogoKeine Creative Commons Lizenz - es gelten die Rechteeinräumung und das deutsche Urheberrecht