Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks

The escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have sh...

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Detalles Bibliográficos
Autores: Luengo Viñuela, Marcos, Román Gallego, Jesús-ángel
Tipo de recurso: artículo
Fecha de publicación:2024
País:España
Institución:Universidad de Salamanca (USAL)
Repositorio:GREDOS. Repositorio Institucional de la Universidad de Salamanca
OAI Identifier:oai:gredos.usal.es:10366/162537
Acceso en línea:http://hdl.handle.net/10366/162537
Access Level:acceso abierto
Palabra clave:DDoS
IoT Networks
AI
cybersecurity
SLR
ML
Machine Learning
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spelling Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT NetworksLuengo Viñuela, MarcosRomán Gallego, Jesús-ángelDDoSIoT NetworksAIcybersecuritySLRMLMachine LearningThe escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have shown efficacy in this realm. This study conducts a systematic review of literature from 2018 to 2023, focusing on DDoS attack detection in IoT Networks using deep learning techniques. Employing the PRISMA methodology, the review identifies and evaluates studies, synthesizing key findings/2**. It highlights that incorporating deep learning significantly enhances DDoS attack detection precision and efficiency, achieving detection rates between 94 % and 99 %. Despite progress, challenges persist, such as limited training data and IoT device processing constraints with large data volumes. This review underscores the importance of addressing these challenges to improve DDoS attack detection in IoT Networks. The research's significance lies in IoT's growing importance and security concerns. It contributes by showcasing current state-of-the-art DDoS detection through deep learning while outlining persistent challenges. Recognizing deep learning's effectiveness sets the stage for refining IoT security protocols, and moreover, by identifying challenges, the research informs strategies to enhance IoT security, fostering a resilient framework.Ediciones Universidad de Salamanca (España)202520252024info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10366/162537reponame:GREDOS. Repositorio Institucional de la Universidad de Salamancainstname:Universidad de Salamanca (USAL)info:eu-repo/semantics/openAccessoai:gredos.usal.es:10366/1625372026-06-07T06:28:51Z
dc.title.none.fl_str_mv Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
title Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
spellingShingle Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
Luengo Viñuela, Marcos
DDoS
IoT Networks
AI
cybersecurity
SLR
ML
Machine Learning
title_short Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
title_full Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
title_fullStr Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
title_full_unstemmed Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
title_sort Systematic Literature Review of Machine Learning Models for Detecting DDoS Attacks in IoT Networks
dc.creator.none.fl_str_mv Luengo Viñuela, Marcos
Román Gallego, Jesús-ángel
author Luengo Viñuela, Marcos
author_facet Luengo Viñuela, Marcos
Román Gallego, Jesús-ángel
author_role author
author2 Román Gallego, Jesús-ángel
author2_role author
dc.subject.none.fl_str_mv DDoS
IoT Networks
AI
cybersecurity
SLR
ML
Machine Learning
topic DDoS
IoT Networks
AI
cybersecurity
SLR
ML
Machine Learning
description The escalating integration of Internet of Things (IoT) devices has led to a surge in data generation within networks, consequently elevating the vulnerability to Distributed Denial of Service (DDoS) attacks. Detecting such attacks in IoT Networks is critical, and Machine Learning (ML) models have shown efficacy in this realm. This study conducts a systematic review of literature from 2018 to 2023, focusing on DDoS attack detection in IoT Networks using deep learning techniques. Employing the PRISMA methodology, the review identifies and evaluates studies, synthesizing key findings/2**. It highlights that incorporating deep learning significantly enhances DDoS attack detection precision and efficiency, achieving detection rates between 94 % and 99 %. Despite progress, challenges persist, such as limited training data and IoT device processing constraints with large data volumes. This review underscores the importance of addressing these challenges to improve DDoS attack detection in IoT Networks. The research's significance lies in IoT's growing importance and security concerns. It contributes by showcasing current state-of-the-art DDoS detection through deep learning while outlining persistent challenges. Recognizing deep learning's effectiveness sets the stage for refining IoT security protocols, and moreover, by identifying challenges, the research informs strategies to enhance IoT security, fostering a resilient framework.
publishDate 2024
dc.date.none.fl_str_mv 2024
2025
2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10366/162537
url http://hdl.handle.net/10366/162537
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
publisher.none.fl_str_mv Ediciones Universidad de Salamanca (España)
dc.source.none.fl_str_mv reponame:GREDOS. Repositorio Institucional de la Universidad de Salamanca
instname:Universidad de Salamanca (USAL)
instname_str Universidad de Salamanca (USAL)
reponame_str GREDOS. Repositorio Institucional de la Universidad de Salamanca
collection GREDOS. Repositorio Institucional de la Universidad de Salamanca
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,812429

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