An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques

The news about distributed denial of service (DDoS) attacks is rapidly increased around the world. Many services of companies and/or governments are victims of the attack. The main purpose of DDoS attacks is to overload the service for a long time, rather than to steal money or data from the targets...

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Main Author:	Ahmed Marwan, Idrees Aleesa
Format:	Thesis
Language:	English English English
Published:	2021
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/1771/2/AHMED%20MARWAN%20IDREES%20ALEESA%20-%20declaration.pdf http://eprints.uthm.edu.my/1771/1/AHMED%20MARWAN%20IDREES%20ALEESA%20-%2024p.pdf http://eprints.uthm.edu.my/1771/3/AHMED%20MARWAN%20IDREES%20ALEESA%20-%20fulltext.pdf
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spelling	my-uthm-ep.17712021-10-11T01:36:20Z An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques 2021-03 Ahmed Marwan, Idrees Aleesa TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television The news about distributed denial of service (DDoS) attacks is rapidly increased around the world. Many services of companies and/or governments are victims of the attack. The main purpose of DDoS attacks is to overload the service for a long time, rather than to steal money or data from the targets. Since the user might not re-use services jammed by crackers, a company attacked by the crackers will lose many benefits. Major challenges are faced by the researchers are the unavailability of the dataset such as “no labelled DDoS attacks for IPv6, no data available online for download or use, few datasets on the internet but the security institutes or researchers who own it are kept private even for the research purposes”. In this research, I developed a DDoS-IPv6 dataset from real attacks traffic that contains 96 extracted features, the generated IPv6-DDoS dataset where had been collected by capturing attacks packets can be converted into network flows that contain rich metadata about the statistics of each flow, which are composed of the captured packet data. These flows are structured in the form of tabular data and contain both continuous and categorical features. Then deployed deep learning technique as intrusion detection system on the developed dataset, moreover optimised deep learning hyperparameters (i.e. the number of hidden layers/neurons, etc.) in order to find the optimal deep learning model, and check if the optimisation of layers/neurons would contribute to improving the accuracy. Accordingly, the result of the optimal deep learning technique for the four models with the developed dataset DDoS-IPv6 are between 99.79% and 99.996% and losses are between 0.0014% and 0.781%. I found that all the techniques succeeded to classify/detect IPv6 attacks and this will lead to new further research that needs to be developed in this area. 2021-03 Thesis http://eprints.uthm.edu.my/1771/ http://eprints.uthm.edu.my/1771/2/AHMED%20MARWAN%20IDREES%20ALEESA%20-%20declaration.pdf text en staffonly http://eprints.uthm.edu.my/1771/1/AHMED%20MARWAN%20IDREES%20ALEESA%20-%2024p.pdf text en public http://eprints.uthm.edu.my/1771/3/AHMED%20MARWAN%20IDREES%20ALEESA%20-%20fulltext.pdf text en validuser phd doctoral Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronics Engineering
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Ahmed Marwan, Idrees Aleesa An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques
description	The news about distributed denial of service (DDoS) attacks is rapidly increased around the world. Many services of companies and/or governments are victims of the attack. The main purpose of DDoS attacks is to overload the service for a long time, rather than to steal money or data from the targets. Since the user might not re-use services jammed by crackers, a company attacked by the crackers will lose many benefits. Major challenges are faced by the researchers are the unavailability of the dataset such as “no labelled DDoS attacks for IPv6, no data available online for download or use, few datasets on the internet but the security institutes or researchers who own it are kept private even for the research purposes”. In this research, I developed a DDoS-IPv6 dataset from real attacks traffic that contains 96 extracted features, the generated IPv6-DDoS dataset where had been collected by capturing attacks packets can be converted into network flows that contain rich metadata about the statistics of each flow, which are composed of the captured packet data. These flows are structured in the form of tabular data and contain both continuous and categorical features. Then deployed deep learning technique as intrusion detection system on the developed dataset, moreover optimised deep learning hyperparameters (i.e. the number of hidden layers/neurons, etc.) in order to find the optimal deep learning model, and check if the optimisation of layers/neurons would contribute to improving the accuracy. Accordingly, the result of the optimal deep learning technique for the four models with the developed dataset DDoS-IPv6 are between 99.79% and 99.996% and losses are between 0.0014% and 0.781%. I found that all the techniques succeeded to classify/detect IPv6 attacks and this will lead to new further research that needs to be developed in this area.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Ahmed Marwan, Idrees Aleesa
author_facet	Ahmed Marwan, Idrees Aleesa
author_sort	Ahmed Marwan, Idrees Aleesa
title	An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques
title_short	An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques
title_full	An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques
title_fullStr	An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques
title_full_unstemmed	An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques
title_sort	intrusion detection system for ddos flooding attacks on ipv6 networks using deep learning techniques
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronics Engineering
publishDate	2021
url	http://eprints.uthm.edu.my/1771/2/AHMED%20MARWAN%20IDREES%20ALEESA%20-%20declaration.pdf http://eprints.uthm.edu.my/1771/1/AHMED%20MARWAN%20IDREES%20ALEESA%20-%2024p.pdf http://eprints.uthm.edu.my/1771/3/AHMED%20MARWAN%20IDREES%20ALEESA%20-%20fulltext.pdf
_version_	1747830864269541376

An intrusion detection system for DDoS flooding attacks on IPv6 networks using deep learning techniques

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