On resource consumption of machine learning in communications network security

As the complexity of communication networks continues to increase, driven by a diverse array of devices, services and applications, the adoption of Machine Learning (ML) has seen a significant rise to address various challenges ranging from management to security. Regarding network security, the app...

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Detalhes bibliográficos
Autores: Hoque, MM, Ahmad, I, Suomalainen, J, Dini, P, Tahir, M
Formato: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Recursos:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
Repositorio:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
OAI Identifier:oai:cttc.fundanetsuite.com:p8769
Acesso em linha:https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8769
Access Level:acceso abierto
Palavra-chave:Security
6G
Distributed security
Network security
Resource consumption
Resource efficiency
Machine learning
ML
6G security
Sustainability
DNN
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spelling On resource consumption of machine learning in communications network securityHoque, MMAhmad, ISuomalainen, JDini, PTahir, MSecurity6GDistributed securityNetwork securityResource consumptionResource efficiencyMachine learningML6G securitySustainabilityDNNAs the complexity of communication networks continues to increase, driven by a diverse array of devices, services and applications, the adoption of Machine Learning (ML) has seen a significant rise to address various challenges ranging from management to security. Regarding network security, the application of ML ranges from preventive measures to detection and remediation due to its ability to dynamically learn and adapt to evolving threat landscapes. However, ML requires a significant amount of resources, mainly due to the fact that ML operates on data, and the volumes of data are consistently rising. This review article explores the resource consumption aspect of ML techniques used for network security and provides a comprehensive review of the current state of research. Moreover, we propose a taxonomy that can be used to classify the methods through which the resource consumption can be reduced for different ML-based network security implementations. The focus of the study encompasses several key aspects related to resource consumption, including energy, computing, memory, latency, bandwidth, and human resources. These resources are critical in improving the efficiency and optimizing the reliability and sustainability of network security solutions. Furthermore, based on an extensive literature review, we summarize key points regarding optimizing resource consumption in ML-based network security solutions. Finally, the challenges and future research directions for resource-efficient, ML-based network security solutions are outlined to aid in the advancement of research in this area.Elsevier2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8769COMPUTER NETWORKSISSN: 13891286ISSNe: 18727069reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)Inglésinfo:eu-repo/semantics/openAccessoai:cttc.fundanetsuite.com:p87692026-06-17T11:44:47Z
dc.title.none.fl_str_mv On resource consumption of machine learning in communications network security
title On resource consumption of machine learning in communications network security
spellingShingle On resource consumption of machine learning in communications network security
Hoque, MM
Security
6G
Distributed security
Network security
Resource consumption
Resource efficiency
Machine learning
ML
6G security
Sustainability
DNN
title_short On resource consumption of machine learning in communications network security
title_full On resource consumption of machine learning in communications network security
title_fullStr On resource consumption of machine learning in communications network security
title_full_unstemmed On resource consumption of machine learning in communications network security
title_sort On resource consumption of machine learning in communications network security
dc.creator.none.fl_str_mv Hoque, MM
Ahmad, I
Suomalainen, J
Dini, P
Tahir, M
author Hoque, MM
author_facet Hoque, MM
Ahmad, I
Suomalainen, J
Dini, P
Tahir, M
author_role author
author2 Ahmad, I
Suomalainen, J
Dini, P
Tahir, M
author2_role author
author
author
author
dc.subject.none.fl_str_mv Security
6G
Distributed security
Network security
Resource consumption
Resource efficiency
Machine learning
ML
6G security
Sustainability
DNN
topic Security
6G
Distributed security
Network security
Resource consumption
Resource efficiency
Machine learning
ML
6G security
Sustainability
DNN
description As the complexity of communication networks continues to increase, driven by a diverse array of devices, services and applications, the adoption of Machine Learning (ML) has seen a significant rise to address various challenges ranging from management to security. Regarding network security, the application of ML ranges from preventive measures to detection and remediation due to its ability to dynamically learn and adapt to evolving threat landscapes. However, ML requires a significant amount of resources, mainly due to the fact that ML operates on data, and the volumes of data are consistently rising. This review article explores the resource consumption aspect of ML techniques used for network security and provides a comprehensive review of the current state of research. Moreover, we propose a taxonomy that can be used to classify the methods through which the resource consumption can be reduced for different ML-based network security implementations. The focus of the study encompasses several key aspects related to resource consumption, including energy, computing, memory, latency, bandwidth, and human resources. These resources are critical in improving the efficiency and optimizing the reliability and sustainability of network security solutions. Furthermore, based on an extensive literature review, we summarize key points regarding optimizing resource consumption in ML-based network security solutions. Finally, the challenges and future research directions for resource-efficient, ML-based network security solutions are outlined to aid in the advancement of research in this area.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8769
url https://cttc.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=8769
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv COMPUTER NETWORKS
ISSN: 13891286
ISSNe: 18727069
reponame:r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname:Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
instname_str Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
reponame_str r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
collection r-CTTC. Repositorio Institucional Producción Científica del Centre Tecnològic de Telecomunicacions de Catalunya (CTTC)
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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