TEAM: A trust evaluation and management framework in context-enabled vehicular ad-hoc networks.

Journal article


Ahmad, F., Franqueira, Virginia N. L. and Adnane, Asma 2018. TEAM: A trust evaluation and management framework in context-enabled vehicular ad-hoc networks. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2837887
AuthorsAhmad, F., Franqueira, Virginia N. L. and Adnane, Asma
Abstract

Vehicular ad-hoc network (VANET) provides a unique platform for vehicles to intelligently exchange critical information, such as collision avoidance messages. It is, therefore, paramount that this information remains reliable and authentic, i.e., originated from a legitimate and trusted vehicle. Trust establishment among vehicles can ensure security of a VANET by identifying dishonest vehicles and revoking messages with malicious content. For this purpose, several trust models (TMs) have been proposed but, currently, there is no effective way to compare how they would behave in practice under adversary conditions. To this end, we propose a novel trust evaluation and management (TEAM) framework, which serves as a unique paradigm for the design, management, and evaluation of TMs in various contexts and in presence of malicious vehicles. Our framework incorporates an asset-based threat model and ISO-based risk assessment for the identification of attacks against critical risks. The TEAM has been built using VEINS, an open source simulation environment which incorporates SUMO traffic simulator and OMNET++ discrete event simulator. The framework created has been tested with the implementation of three types of TMs (data oriented, entity oriented, and hybrid) under four different contexts of VANET based on the mobility of both honest and malicious vehicles. Results indicate that the TEAM is effective to simulate a wide range of TMs, where the efficiency is evaluated against different quality of service and security-related criteria. Such framework may be instrumental for planning smart cities and for car manufacturers.

Vehicular ad-hoc network (VANET) provides a unique platform for vehicles to intelligently
exchange critical information, such as collision avoidance messages. It is, therefore, paramount that this
information remains reliable and authentic, i.e., originated from a legitimate and trusted vehicle. Trust
establishment among vehicles can ensure security of a VANET by identifying dishonest vehicles and
revoking messages with malicious content. For this purpose, several trust models (TMs) have been proposed
but, currently, there is no effective way to compare how they would behave in practice under adversary
conditions. To this end, we propose a novel trust evaluation and management (TEAM) framework, which
serves as a unique paradigm for the design, management, and evaluation of TMs in various contexts and in
presence of malicious vehicles. Our framework incorporates an asset-based threat model and ISO-based risk
assessment for the identification of attacks against critical risks. The TEAM has been built using VEINS,
an open source simulation environment which incorporates SUMO traffic simulator and OMNET++
discrete event simulator. The framework created has been tested with the implementation of three types
of TMs (data oriented, entity oriented, and hybrid) under four different contexts of VANET based on the
mobility of both honest and malicious vehicles. Results indicate that the TEAM is effective to simulate a
wide range of TMs, where the efficiency is evaluated against different quality of service and security-related
criteria. Such framework may be instrumental for planning smart cities and for car manufacturers.

KeywordsVehicular networks; Trust management; Smart cities; Security; Intelligent transport systems (ITS); Simulation
Year2018
JournalIEEE Access
PublisherIEEE
ISSN21693536
Digital Object Identifier (DOI)https://doi.org/10.1109/ACCESS.2018.2837887
Web address (URL)http://hdl.handle.net/10545/622781
hdl:10545/622781
Publication dates25 May 2018
Publication process dates
Deposited04 Jul 2018, 11:55
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ContributorsUniversity of Derby
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