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image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Wireless Networksarrow_drop_down
image/svg+xml Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao Closed Access logo, derived from PLoS Open Access logo. This version with transparent background. http://commons.wikimedia.org/wiki/File:Closed_Access_logo_transparent.svg Jakob Voss, based on art designer at PLoS, modified by Wikipedia users Nina and Beao
Wireless Networks
Article . 2016 . Peer-reviewed
License: Springer TDM
Data sources: Crossref
https://doi.org/10.1109/nca.20...
Article . 2014 . Peer-reviewed
Data sources: Crossref
DBLP
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Data sources: DBLP
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Article . 2017
Data sources: DBLP
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Dynamic Attribute Based Vehicle Authentication

Authors: Shlomi Dolev; Lukasz Krzywiecki; Nisha Panwar; Michael Segal 0001;

Dynamic Attribute Based Vehicle Authentication

Abstract

Modern vehicles are proficient in establishing a spontaneous connection over a wireless radio channel, synchronizing actions and information. Security infrastructure is most important in such a sensitive scope of vehicle communication for coordinating actions and avoiding accidents on the road. One of the first security issues that need to be established is authentication via IEEE 1609.2 security infrastructure. According to our preliminary work, vehicle owners are bound to preprocess a certificate from the certificate authority. The certificate carries vehicle static attributes (e.g., licence number, brand and color) certified together with the vehicle public key in a monolithic manner. Nevertheless, a malicious vehicle might clone the static attributes to impersonate a specific vehicle. Therefore, in this paper we consider a resource expensive attack scenario involving multiple malicious vehicles with identical visual static attributes. Apparently, dynamic attributes (e.g., location and direction) can uniquely define a vehicle and can be utilized to resolve the true identity of the vehicle. However, unlike static attributes, dynamic attributes cannot be signed by a trusted authority beforehand. We propose an approach to verify the coupling between non-certified dynamic attributes and certified static attributes on an auxiliary communication channel, for example, a modulated laser beam. Furthermore, we illustrate that the proposed approach can be used to facilitate the usage of existing authentication protocols such as NAXOS, in the new scope of ad-hoc vehicle networks. We use BAN logic to verify the security claims of the protocol against the passive and active interception.

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    popularity
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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
13
Top 10%
Top 10%
Average
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