Adapting Organic Computing Architectures to an Automotive Environment to Increase Safety & Security
Abstract
Modern cars are very complex systems operating in a diverse environment. Today they incorporate an internal network connecting an array of actuators and sensors to ECUs (Electronic Control Units) which implement basic functions and advanced driver assistance systems. Opening these networks to outside communication channels (like Car-to-X-communication) new possibilities but also new attack vectors arise. Recent work has shown that it is possible for an attacker to infiltrate the ECU network insides a vehicle using these external communication channels. Any attack on the security of a vehicle comes implies an impact on the safety of road traffic. This paper discusses the possibilities of using architectures suggested by Organic Computing to reduce these arising security risks and therefore improve safety. A proposed architecture is implemented in a demonstrator and evaluated using different attack scenarios.
- Citation
- BibTeX
Lamshöft, K., Altschaffel, R. & Dittmann, J.,
(2017).
Adapting Organic Computing Architectures to an Automotive Environment to Increase Safety & Security.
In:
Dencker, P., Klenk, H., Keller, H. B. & Plödererder, E.
(Hrsg.),
Automotive - Safety & Security 2017 - Sicherheit und Zuverlässigkeit für automobile Informationstechnik.
Gesellschaft für Informatik, Bonn.
(S. 103-119).
@article{mci/Lamshöft2017,
author = {Lamshöft, Kevin AND Altschaffel, Robert AND Dittmann, Jana},
title = {Adapting Organic Computing Architectures to an Automotive Environment to Increase Safety & Security},
journal = {},
volume = {},
number = {},
year = {2017},
,
pages = { 103-119 }
}
author = {Lamshöft, Kevin AND Altschaffel, Robert AND Dittmann, Jana},
title = {Adapting Organic Computing Architectures to an Automotive Environment to Increase Safety & Security},
journal = {},
volume = {},
number = {},
year = {2017},
,
pages = { 103-119 }
}
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More Info
ISBN: 978-3-88579-663-3
ISSN: 1617-5468
xmlui.MetaDataDisplay.field.date: 2017
Language:
(en)

Content Type: Text/Journal Article