TEAM: A trust evaluation and management framework in context-enabled vehicular ad-hoc networks.
|Authors||Ahmad, F., Franqueira, Virginia N. L. and Adnane, Asma|
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
|Keywords||Vehicular networks; Trust management; Smart cities; Security; Intelligent transport systems (ITS); Simulation|
|Digital Object Identifier (DOI)||https://doi.org/10.1109/ACCESS.2018.2837887|
|Web address (URL)||http://hdl.handle.net/10545/622781|
|Publication dates||25 May 2018|
|Publication process dates|
|Deposited||04 Jul 2018, 11:55|
Archived with thanks to IEEE Access
|Contributors||University of Derby|
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