Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments

[EN] Over the last few years, Software Defined Networking (SDN) paradigm has become an emerging architecture to design future networks and to meet new application demands. SDN provides resources for improving network control and management by separating control and data plane, and the logical contro...

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Autores: Novaes, Matheus P., Carvalho, Luiz F., Lemes Proença, Mario Jr., Lloret, Jaime|||0000-0002-0862-0533
Tipo de recurso: artículo
Fecha de publicación:2021
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:10251/188831
Acceso en línea:https://riunet.upv.es/handle/10251/188831
Access Level:acceso abierto
Palabra clave:Adversarial attacks
DDoS
Deep Learning
GAN
SDN
INGENIERIA TELEMATICA
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spelling Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environmentsNovaes, Matheus P.Carvalho, Luiz F.Lemes Proença, Mario Jr.Lloret, Jaime|||0000-0002-0862-0533Adversarial attacksDDoSDeep LearningGANSDNINGENIERIA TELEMATICA[EN] Over the last few years, Software Defined Networking (SDN) paradigm has become an emerging architecture to design future networks and to meet new application demands. SDN provides resources for improving network control and management by separating control and data plane, and the logical control is centralized in a controller. However, the centralized control logic can be an ideal target for malicious attacks, mainly Distributed Denial of Service (DDoS) attacks. Recently, Deep Learning has become a powerful technique applied in cybersecurity, and many Network Intrusion Detection (NIDS) have been proposed in recent researches. Some studies have indicated that deep neural networks are sensitive in detecting adversarial attacks. Adversarial attacks are instances with certain perturbations that cause deep neural networks to misclassify. In this paper, we proposed a detection and defense system based on Adversarial training in SDN, which uses Generative Adversarial Network (GAN) framework for detecting DDoS attacks and applies adversarial training to make the system less sensitive to adversarial attacks. The proposed system includes well-defined modules that enable continuous traffic monitoring using IP flow analysis, enabling the anomaly detection system to act in near-real-time. We conducted the experiments on two distinct scenarios, with emulated data and the public dataset CICDDoS 2019. Experimental results demonstrated that the system efficiently detected up-to-date common types of DDoS attacks compared to other approaches.This work has been partially supported by the National Council for Scientific and Technological Development (CNPq) of Brazil under Grant of Project 310668/2019-0 and by SETI, Brazil/Fundacao Araucaria due to the concession of scholarships; by the "Ministerio de Economia y Competitividad, Spain"in the "Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento"within the project under Grant TIN2017-84802-C2-1-P.ElsevierDepartamento de ComunicacionesEscuela Politécnica Superior de GandiaAgencia Estatal de InvestigaciónConselho Nacional de Desenvolvimento Científico e Tecnológico, BrasilRepositorio Institucional de la Universitat Politècnica de València Riunet20212021-12-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfapplication/pdfhttps://riunet.upv.es/handle/10251/188831reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)InglésengAgencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-84802-C2-1-P RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICAConselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 310668%2F2019-0open accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) http://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/1888312026-06-13T07:49:27Z
dc.title.none.fl_str_mv Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
title Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
spellingShingle Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
Novaes, Matheus P.
Adversarial attacks
DDoS
Deep Learning
GAN
SDN
INGENIERIA TELEMATICA
title_short Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
title_full Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
title_fullStr Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
title_full_unstemmed Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
title_sort Adversarial Deep Learning approach detection and defense against DDoS attacks in SDN environments
dc.creator.none.fl_str_mv Novaes, Matheus P.
Carvalho, Luiz F.
Lemes Proença, Mario Jr.
Lloret, Jaime|||0000-0002-0862-0533
author Novaes, Matheus P.
author_facet Novaes, Matheus P.
Carvalho, Luiz F.
Lemes Proença, Mario Jr.
Lloret, Jaime|||0000-0002-0862-0533
author_role author
author2 Carvalho, Luiz F.
Lemes Proença, Mario Jr.
Lloret, Jaime|||0000-0002-0862-0533
author2_role author
author
author
dc.contributor.none.fl_str_mv Departamento de Comunicaciones
Escuela Politécnica Superior de Gandia
Agencia Estatal de Investigación
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil
Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Adversarial attacks
DDoS
Deep Learning
GAN
SDN
INGENIERIA TELEMATICA
topic Adversarial attacks
DDoS
Deep Learning
GAN
SDN
INGENIERIA TELEMATICA
description [EN] Over the last few years, Software Defined Networking (SDN) paradigm has become an emerging architecture to design future networks and to meet new application demands. SDN provides resources for improving network control and management by separating control and data plane, and the logical control is centralized in a controller. However, the centralized control logic can be an ideal target for malicious attacks, mainly Distributed Denial of Service (DDoS) attacks. Recently, Deep Learning has become a powerful technique applied in cybersecurity, and many Network Intrusion Detection (NIDS) have been proposed in recent researches. Some studies have indicated that deep neural networks are sensitive in detecting adversarial attacks. Adversarial attacks are instances with certain perturbations that cause deep neural networks to misclassify. In this paper, we proposed a detection and defense system based on Adversarial training in SDN, which uses Generative Adversarial Network (GAN) framework for detecting DDoS attacks and applies adversarial training to make the system less sensitive to adversarial attacks. The proposed system includes well-defined modules that enable continuous traffic monitoring using IP flow analysis, enabling the anomaly detection system to act in near-real-time. We conducted the experiments on two distinct scenarios, with emulated data and the public dataset CICDDoS 2019. Experimental results demonstrated that the system efficiently detected up-to-date common types of DDoS attacks compared to other approaches.
publishDate 2021
dc.date.none.fl_str_mv 2021
2021-12-01
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/10251/188831
url https://riunet.upv.es/handle/10251/188831
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://dx.doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016 TIN2017-84802-C2-1-P RED COGNITIVA DEFINIDA POR SOFTWARE PARA OPTIMIZAR Y SECURIZAR TRAFICO DE INTERNET DE LAS COSAS CON INFORMACION CRITICA
Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil https://doi.org/10.13039/501100003593 310668%2F2019-0
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/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 - No comercial - Sin obra derivada (by-nc-nd)
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
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
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score 15,301603

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