Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic

[EN] Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety...

Descripción completa

Detalles Bibliográficos
Autores: Manderna, Ankit, Kumar, Sushil, Dohare, Upasana, Aljaidi, Mohammad, Kaiwartya, Omprakash, Lloret, Jaime|||0000-0002-0862-0533
Tipo de recurso: artículo
Fecha de publicación:2023
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/204175
Acceso en línea:https://riunet.upv.es/handle/10251/204175
Access Level:acceso abierto
Palabra clave:VANET
Intrusion detection
Deep learning
Long short-term memory
Convolution neural network
INGENIERÍA TELEMÁTICA
id ES_2203cd7aeab9b3bbd14c33fe4ae8b1b1
oai_identifier_str oai:riunet.upv.es:10251/204175
network_acronym_str ES
network_name_str España
repository_id_str
spelling Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant MetaheuristicManderna, AnkitKumar, SushilDohare, UpasanaAljaidi, MohammadKaiwartya, OmprakashLloret, Jaime|||0000-0002-0862-0533VANETIntrusion detectionDeep learningLong short-term memoryConvolution neural networkINGENIERÍA TELEMÁTICA[EN] Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model's performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.This work is supported by the SC&SS, Jawaharlal Nehru University, New Delhi, India. This research is supported by the B11 unit of assessment, Centre for Computing and Informatics Research Centre, Department of Computer Science, Nottingham Trent University, UK.MDPI AGDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaJawaharlal Nehru UniversityNottingham Trent UniversityRepositorio Institucional de la Universitat Politècnica de València Riunet20232023-11-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/204175reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2041752026-06-13T07:49:27Z
dc.title.none.fl_str_mv Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
title Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
spellingShingle Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
Manderna, Ankit
VANET
Intrusion detection
Deep learning
Long short-term memory
Convolution neural network
INGENIERÍA TELEMÁTICA
title_short Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
title_full Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
title_fullStr Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
title_full_unstemmed Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
title_sort Vehicular Network Intrusion Detection Using a Cascaded Deep Learning Approach with Multi-Variant Metaheuristic
dc.creator.none.fl_str_mv Manderna, Ankit
Kumar, Sushil
Dohare, Upasana
Aljaidi, Mohammad
Kaiwartya, Omprakash
Lloret, Jaime|||0000-0002-0862-0533
author Manderna, Ankit
author_facet Manderna, Ankit
Kumar, Sushil
Dohare, Upasana
Aljaidi, Mohammad
Kaiwartya, Omprakash
Lloret, Jaime|||0000-0002-0862-0533
author_role author
author2 Kumar, Sushil
Dohare, Upasana
Aljaidi, Mohammad
Kaiwartya, Omprakash
Lloret, Jaime|||0000-0002-0862-0533
author2_role author
author
author
author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Jawaharlal Nehru University
Nottingham Trent University
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv VANET
Intrusion detection
Deep learning
Long short-term memory
Convolution neural network
INGENIERÍA TELEMÁTICA
topic VANET
Intrusion detection
Deep learning
Long short-term memory
Convolution neural network
INGENIERÍA TELEMÁTICA
description [EN] Vehicle malfunctions have a direct impact on both human and road safety, making vehicle network security an important and critical challenge. Vehicular ad hoc networks (VANETs) have grown to be indispensable in recent years for enabling intelligent transport systems, guaranteeing traffic safety, and averting collisions. However, because of numerous types of assaults, such as Distributed Denial of Service (DDoS) and Denial of Service (DoS), VANETs have significant difficulties. A powerful Network Intrusion Detection System (NIDS) powered by Artificial Intelligence (AI) is required to overcome these security issues. This research presents an innovative method for creating an AI-based NIDS that uses Deep Learning methods. The suggested model specifically incorporates the Self Attention-Based Bidirectional Long Short-Term Memory (SA-BiLSTM) for classification and the Cascaded Convolution Neural Network (CCNN) for learning high-level features. The Multi-variant Gradient-Based Optimization algorithm (MV-GBO) is applied to improve CCNN and SA-BiLSTM further to enhance the model's performance. Additionally, information gained using MV-GBO-based feature extraction is employed to enhance feature learning. The effectiveness of the proposed model is evaluated on reliable datasets such as KDD-CUP99, ToN-IoT, and VeReMi, which are utilized on the MATLAB platform. The proposed model achieved 99% accuracy on all the datasets.
publishDate 2023
dc.date.none.fl_str_mv 2023
2023-11-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/204175
url https://riunet.upv.es/handle/10251/204175
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869404552439005184
score 15,301603

Similares en LA Referencia