A novel context-based risk assessment approach in vehicular networks

Conference item


Ahmad, F. and Adnane, Asma 2016. A novel context-based risk assessment approach in vehicular networks. IEEE. https://doi.org/10.1109/WAINA.2016.60
AuthorsAhmad, F. and Adnane, Asma
Abstract

Vehicular Networks (VANET) are the largest real life application of ad-hoc networks where nodes are represented via fast moving vehicles. As VANET is characterised with several unique features such as the high mobility, random distribution and short connection times, etc. establishing security becomes very challenging, and identifying security threats and possible attacks become very critical. Thus, identifying, managing and exploiting risk across the different components of VANET have become increasingly important to ensure the success and the robustness of the VANET components and its applications. As VANET include different contexts, the risk posed by particular attack may have different impact, i.e., the risk caused by an attack in rural and urban area may have different consequences on the network. To this end, we propose a novel context-based risk assessment methodology to identify attacks in different contexts of VANET. The main aim of the paper is twofold: 1) First, we identified vulnerabilities, threats and attacks on important assets in VANET, and (2) secondly, we proposed a novel context-based risk assessment approach in VANET to identify the risks encountered by attacks in VANET in various contexts.

KeywordsVehicular networks; Security; Risk assessment; Wireless technology
Year2016
JournalProceedings of the 30th International Conference on Advanced Information Networking and Applications Workshops (WAINA), Le Régent Congress Centre, Crans-Montana, Switzerland, March 23-25,
PublisherIEEE
Digital Object Identifier (DOI)https://doi.org/10.1109/WAINA.2016.60
Web address (URL)http://hdl.handle.net/10545/620830
hdl:10545/620830
ISBN978-1-5090-2461-2
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Open
Publication dates23 Mar 2016
Publication process dates
Deposited14 Nov 2016, 12:36
ContributorsUniversity of Derby
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