Network Intrusion Detection System for Denial-of-Service attack detection in 5G

The number of connected devices in the network continues to increase year by year, specially with the introduction of fifth-generation (5G) technology, which offers higher capacity to accommodate the growing demand. However, these devices, such as Internet of Things (IoT), are vulnerable to Denial o...

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Detalhes bibliográficos
Autor: Chriki Zerrouk, Fatima Zohra
Tipo de documento: dissertação
Data de publicação:2024
País:España
Recursos:Universitat Politècnica de Catalunya (UPC)
Repositório:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglês
OAI Identifier:oai:upcommons.upc.edu:2117/417190
Acesso em linha:https://hdl.handle.net/2117/417190
Access Level:Acceso aberto
Palavra-chave:Machine learning
Deep learning (Machine learning)
Computer security
Sistema de Detecció d'Intrusions
Aprenentatge Federat
5G
Aprenentatge Automàtic
Aprenentatge Profund
DoS
DDoS
Denegació de Servei
Ciberseguretat
Xarxa
Intrusion Detection System
Federated Learning
Machine Learning
Deep Learning
Denial of Service
Cybersecurity
Network
Aprenentatge automàtic
Aprenentatge profund
Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
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oai_identifier_str oai:upcommons.upc.edu:2117/417190
network_acronym_str ES
network_name_str España
repository_id_str
spelling Network Intrusion Detection System for Denial-of-Service attack detection in 5GChriki Zerrouk, Fatima ZohraMachine learningDeep learning (Machine learning)Computer securitySistema de Detecció d'IntrusionsAprenentatge Federat5GAprenentatge AutomàticAprenentatge ProfundDoSDDoSDenegació de ServeiCiberseguretatXarxaIntrusion Detection SystemFederated LearningMachine LearningDeep LearningDenial of ServiceCybersecurityNetworkAprenentatge automàticAprenentatge profundSeguretat informàticaÀrees temàtiques de la UPC::Informàtica::Seguretat informàticaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàticThe number of connected devices in the network continues to increase year by year, specially with the introduction of fifth-generation (5G) technology, which offers higher capacity to accommodate the growing demand. However, these devices, such as Internet of Things (IoT), are vulnerable to Denial of Service (DoS) attacks if they are not properly secured. A DoS attack inundates a network or device with excessive traffic, overwhelming it until it is inaccessible to legitimate users. This vulnerability poses significant risks to critical services such as healthcare, energy, and transportation. This thesis addresses the challenge of detecting DoS attacks in 5G networks by designing and developing an Intrusion Detection System (IDS) based on Deep Learning (DL). The proposed IDS monitors network traffic in real-time to identify DoS patterns and alerts administrators when potential attacks are detected. The designed IDS is composed of two neural network models: the ADC model, which classifies benign network traffic flows from flows containing DoS patterns, and the DoSC model, which categorizes the specific type of DoS attack. These ML models are trained using the Federated Learning (FL) paradigm, which involves three clients, each utilizing a portion of data from a public 5G network traffic dataset. This approach enables the IDS to learn from diverse data sources without compromising data privacy. The IDS models were evaluated on unseen data, achieving an accuracy of 100%, which demonstrates the high capability of the IDS to detect DoS patterns in network flows. The developed IDS was deployed in a simulated environment, where it receives network traffic flows, analyses the network to alert administrators upon detection, and provides an interface for monitoring the detected DoS attack flows.Universitat Politècnica de CatalunyaRodríguez Luna, EvaSimó Mezquita, Ester20242024-06-2820242024-11-07master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2117/417190reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/4171902026-05-27T15:37:01Z
dc.title.none.fl_str_mv Network Intrusion Detection System for Denial-of-Service attack detection in 5G
title Network Intrusion Detection System for Denial-of-Service attack detection in 5G
spellingShingle Network Intrusion Detection System for Denial-of-Service attack detection in 5G
Chriki Zerrouk, Fatima Zohra
Machine learning
Deep learning (Machine learning)
Computer security
Sistema de Detecció d'Intrusions
Aprenentatge Federat
5G
Aprenentatge Automàtic
Aprenentatge Profund
DoS
DDoS
Denegació de Servei
Ciberseguretat
Xarxa
Intrusion Detection System
Federated Learning
Machine Learning
Deep Learning
Denial of Service
Cybersecurity
Network
Aprenentatge automàtic
Aprenentatge profund
Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
title_short Network Intrusion Detection System for Denial-of-Service attack detection in 5G
title_full Network Intrusion Detection System for Denial-of-Service attack detection in 5G
title_fullStr Network Intrusion Detection System for Denial-of-Service attack detection in 5G
title_full_unstemmed Network Intrusion Detection System for Denial-of-Service attack detection in 5G
title_sort Network Intrusion Detection System for Denial-of-Service attack detection in 5G
dc.creator.none.fl_str_mv Chriki Zerrouk, Fatima Zohra
author Chriki Zerrouk, Fatima Zohra
author_facet Chriki Zerrouk, Fatima Zohra
author_role author
dc.contributor.none.fl_str_mv Rodríguez Luna, Eva
Simó Mezquita, Ester
dc.subject.none.fl_str_mv Machine learning
Deep learning (Machine learning)
Computer security
Sistema de Detecció d'Intrusions
Aprenentatge Federat
5G
Aprenentatge Automàtic
Aprenentatge Profund
DoS
DDoS
Denegació de Servei
Ciberseguretat
Xarxa
Intrusion Detection System
Federated Learning
Machine Learning
Deep Learning
Denial of Service
Cybersecurity
Network
Aprenentatge automàtic
Aprenentatge profund
Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
topic Machine learning
Deep learning (Machine learning)
Computer security
Sistema de Detecció d'Intrusions
Aprenentatge Federat
5G
Aprenentatge Automàtic
Aprenentatge Profund
DoS
DDoS
Denegació de Servei
Ciberseguretat
Xarxa
Intrusion Detection System
Federated Learning
Machine Learning
Deep Learning
Denial of Service
Cybersecurity
Network
Aprenentatge automàtic
Aprenentatge profund
Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Seguretat informàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
description The number of connected devices in the network continues to increase year by year, specially with the introduction of fifth-generation (5G) technology, which offers higher capacity to accommodate the growing demand. However, these devices, such as Internet of Things (IoT), are vulnerable to Denial of Service (DoS) attacks if they are not properly secured. A DoS attack inundates a network or device with excessive traffic, overwhelming it until it is inaccessible to legitimate users. This vulnerability poses significant risks to critical services such as healthcare, energy, and transportation. This thesis addresses the challenge of detecting DoS attacks in 5G networks by designing and developing an Intrusion Detection System (IDS) based on Deep Learning (DL). The proposed IDS monitors network traffic in real-time to identify DoS patterns and alerts administrators when potential attacks are detected. The designed IDS is composed of two neural network models: the ADC model, which classifies benign network traffic flows from flows containing DoS patterns, and the DoSC model, which categorizes the specific type of DoS attack. These ML models are trained using the Federated Learning (FL) paradigm, which involves three clients, each utilizing a portion of data from a public 5G network traffic dataset. This approach enables the IDS to learn from diverse data sources without compromising data privacy. The IDS models were evaluated on unseen data, achieving an accuracy of 100%, which demonstrates the high capability of the IDS to detect DoS patterns in network flows. The developed IDS was deployed in a simulated environment, where it receives network traffic flows, analyses the network to alert administrators upon detection, and provides an interface for monitoring the detected DoS attack flows.
publishDate 2024
dc.date.none.fl_str_mv 2024
2024-06-28
2024
2024-11-07
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/417190
url https://hdl.handle.net/2117/417190
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
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
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869413108243496960
score 15.198674

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