Cited By
View all- Alalwany EMahgoub I(2024)Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning SolutionsSensors10.3390/s2402036824:2(368)Online publication date: 8-Jan-2024
A Survey on Vehicular Ad hoc Networks Security Attacks and Countermeasures
Authors: 
Abdelrahman Abuarqoub, Ahmad Alzu'bi, Mohammad Hammoudeh, Arslan Ahmad, Bassam Al-ShargabiAuthors Info & Claims
Abdelrahman Abuarqoub, Ahmad Alzu'bi, Mohammad Hammoudeh, Arslan Ahmad, Bassam Al-ShargabiAuthors Info & ClaimsAbstract
Vehicular Ad hoc Networks (VANETs), a subcategory of Mobile ad hoc networks (MANETs), is a promising approach that is now evolving, gaining attention and momentum. VANET consists of highly mobile nodes and roadside infrastructure to communicate essential information across a network, such as collisions, traffic congestion, and vehicular destinations. Due to its frequent vehicle movement, time-critical response and hybrid architecture, it comes with a plethora of network security and privacy issues ranging from the potential of malicious attacks to the availability of essential information across the network and its overall integrity. There is much research devoted to enabling secure communication in VANETs for the best Intelligent Transport System (ITS) experience. In this paper, we address and discuss the characteristics and security issues including the existing VANETs attacks and preventive approaches to each security attack emphasising the main features and the major drawbacks. The review of these different countermeasures shows their strengths and weaknesses and how they can be implemented independently or in a hybrid model best suited to the situation.
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December 2022
734 pages
ISBN:9781450399050
DOI:10.1145/3584202
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Published: 09 May 2023
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ICFNDS '22
ICFNDS '22: The 6th International Conference on Future Networks & Distributed Systems
December 15, 2022
TAS, Tashkent, Uzbekistan
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View all- Alalwany EMahgoub I(2024)Security and Trust Management in the Internet of Vehicles (IoV): Challenges and Machine Learning SolutionsSensors10.3390/s2402036824:2(368)Online publication date: 8-Jan-2024
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