Security of Connected and Automated Vehicles
The transportation system is rapidly evolving with new connected and automated vehicle (CAV) technologies that integrate CAVs with other vehicles and roadside infrastructure in a cyberphysical system (CPS). Through connectivity, CAVs affect their environments and vice versa, increasing the size of the cyberattack surface and the risk of exploitation of security vulnerabilities by malicious actors. Thus, greater understanding of potential -CAV-CPS cyberattacks and of ways to prevent them is a high priority. In this article the authors describe CAV-CPS cyberattack surfaces and security vulnerabilities, and outline potential cyberattack detection and mitigation strategies. The authors examine emerging technologies—artificial intelligence, software-defined networks, network function virtualization, edge computing, information-centric and virtual dispersive networking, fifth generation (5G) cellular networks, blockchain technology, and quantum and postquantum cryptography—as potential solutions aiding in securing CAVs and transportation infrastructure against existing and future cyberattacks.
- Record URL:
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/07376278
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Authors:
- Chowdhury, Mashrur
- Islam, Mhafuzul
- Khan, Zadid
- Publication Date: 2019-9
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
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Serial:
- The Bridge
- Volume: 49
- Issue Number: 3
- Publisher: National Academy of Engineering
- ISSN: 0737-6278
- Serial URL: http://www.nae.edu/Publications/Bridge.aspx
Subject/Index Terms
- TRT Terms: Artificial intelligence; Autonomous vehicles; Connected vehicles; Information processing; Risk assessment; Security; Software; Wireless LANs
- Candidate Terms: Cyber attacks
- Subject Areas: Data and Information Technology; Highways; Security and Emergencies; Vehicles and Equipment;
Filing Info
- Accession Number: 01719140
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 7 2019 9:17AM