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Title	Prediction And Detection Model For Hierarchical Software-Defined Vehicular Network
ID_Doc	42781
Authors	Amari H.; Khoukhi L.; Belguith L.H.
Year	2022
Published	Proceedings - Conference on Local Computer Networks, LCN
DOI	http://dx.doi.org/10.1109/LCN53696.2022.9843483
Abstract	Vehicle Ad-hoc Network (VANET) is the main component of the intelligent transportation system. With the development of the next-generation intelligent vehicular networks, the latter aims to provide strategic and secure services and communications in roads and smart cities. Due to VANET's unique characteristics, such as high mobility of its nodes, self-organization, distributed network, and frequently changing topology, security, data integrity, and users' privacy information are major concerns. Also, attack prevention is still an open issue. Distributed Denial of Service (DDoS) is one of the most dangerous attacks in VANETs, which aims to flood the system's bandwidth. In this article, we propose a hierarchical architecture for securing Software-Defined Vehicular Network (SDVN) and a security model for predicting and detecting DDoS attacks based on behavioral analysis of nodes achieved by a Markov stochastic process. Simulation results show that our model effectively mitigates DDoS attacks with a high-reliability rate. © 2022 IEEE.
Author Keywords	DDoS attacks; Markov stochastic process; SDN; SDN Controller; SDVN; security model; VANETs

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