An Intelligent Hierarchical Security Framework for VANETs

Vehicular Ad hoc Networks (VANETs) are an emerging type of network that increasingly encompass a larger number of vehicles. They are the basic support for Intelligent Transportation Systems (ITS) and for establishing frameworks which enable communication among road entities and foster the developmen...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Fábio Gonçalves, Joaquim Macedo, Alexandre Santos
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	VANETs security intrusion detection systems machine learning Information technology T58.5-58.64
Acceso en línea:	https://doaj.org/article/1699e5a547e64d90b558803ac9725075
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:1699e5a547e64d90b558803ac9725075
record_format	dspace
spelling	oai:doaj.org-article:1699e5a547e64d90b558803ac97250752021-11-25T17:58:30ZAn Intelligent Hierarchical Security Framework for VANETs10.3390/info121104552078-2489https://doaj.org/article/1699e5a547e64d90b558803ac97250752021-11-01T00:00:00Zhttps://www.mdpi.com/2078-2489/12/11/455https://doaj.org/toc/2078-2489Vehicular Ad hoc Networks (VANETs) are an emerging type of network that increasingly encompass a larger number of vehicles. They are the basic support for Intelligent Transportation Systems (ITS) and for establishing frameworks which enable communication among road entities and foster the development of new applications and services aimed at enhancing driving experience and increasing road safety. However, VANETs’ demanding characteristics make it difficult to implement security mechanisms, creating vulnerabilities easily explored by attackers. The main goal of this work is to propose an Intelligent Hierarchical Security Framework for VANET making use of Machine Learning (ML) algorithms to enhance attack detection, and to define methods for secure communications among entities, assuring strong authentication, privacy, and anonymity. The ML algorithms used in this framework have been trained and tested using vehicle communications datasets, which have been made publicly available, thus providing easily reproducible and verifiable results. The obtained results show that the proposed Intrusion Detection System (IDS) framework is able to detect attacks accurately, with a low False Positive Rate (FPR). Furthermore, results show that the framework can benefit from using different types of algorithms at different hierarchical levels, selecting light and fast processing algorithms in the lower levels, at the cost of accuracy, and using more precise, accurate, and complex algorithms in nodes higher in the hierarchy.Fábio GonçalvesJoaquim MacedoAlexandre SantosMDPI AGarticleVANETssecurityintrusion detection systemsmachine learningInformation technologyT58.5-58.64ENInformation, Vol 12, Iss 455, p 455 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	VANETs security intrusion detection systems machine learning Information technology T58.5-58.64
spellingShingle	VANETs security intrusion detection systems machine learning Information technology T58.5-58.64 Fábio Gonçalves Joaquim Macedo Alexandre Santos An Intelligent Hierarchical Security Framework for VANETs
description	Vehicular Ad hoc Networks (VANETs) are an emerging type of network that increasingly encompass a larger number of vehicles. They are the basic support for Intelligent Transportation Systems (ITS) and for establishing frameworks which enable communication among road entities and foster the development of new applications and services aimed at enhancing driving experience and increasing road safety. However, VANETs’ demanding characteristics make it difficult to implement security mechanisms, creating vulnerabilities easily explored by attackers. The main goal of this work is to propose an Intelligent Hierarchical Security Framework for VANET making use of Machine Learning (ML) algorithms to enhance attack detection, and to define methods for secure communications among entities, assuring strong authentication, privacy, and anonymity. The ML algorithms used in this framework have been trained and tested using vehicle communications datasets, which have been made publicly available, thus providing easily reproducible and verifiable results. The obtained results show that the proposed Intrusion Detection System (IDS) framework is able to detect attacks accurately, with a low False Positive Rate (FPR). Furthermore, results show that the framework can benefit from using different types of algorithms at different hierarchical levels, selecting light and fast processing algorithms in the lower levels, at the cost of accuracy, and using more precise, accurate, and complex algorithms in nodes higher in the hierarchy.
format	article
author	Fábio Gonçalves Joaquim Macedo Alexandre Santos
author_facet	Fábio Gonçalves Joaquim Macedo Alexandre Santos
author_sort	Fábio Gonçalves
title	An Intelligent Hierarchical Security Framework for VANETs
title_short	An Intelligent Hierarchical Security Framework for VANETs
title_full	An Intelligent Hierarchical Security Framework for VANETs
title_fullStr	An Intelligent Hierarchical Security Framework for VANETs
title_full_unstemmed	An Intelligent Hierarchical Security Framework for VANETs
title_sort	intelligent hierarchical security framework for vanets
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/1699e5a547e64d90b558803ac9725075
work_keys_str_mv	AT fabiogoncalves anintelligenthierarchicalsecurityframeworkforvanets AT joaquimmacedo anintelligenthierarchicalsecurityframeworkforvanets AT alexandresantos anintelligenthierarchicalsecurityframeworkforvanets AT fabiogoncalves intelligenthierarchicalsecurityframeworkforvanets AT joaquimmacedo intelligenthierarchicalsecurityframeworkforvanets AT alexandresantos intelligenthierarchicalsecurityframeworkforvanets
_version_	1718411725742538752

An Intelligent Hierarchical Security Framework for VANETs

Ejemplares similares