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Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system

With an increasing number of recent services connected to the Internet, including cloud computing and Internet of Things systems, cyber-attacks have become more challenging. The deep learning approach plays a pertinent role in tracing new attacks in cybersecurity. Recently, researchers suggested a d...

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Main Author: Maseer, Ziadoon Kamil
Format: Thesis
Language:English
English
Published: 2022
Subjects:
Q Science (General)
Online Access:http://eprints.utem.edu.my/id/eprint/28241/1/Hybrid%20weight%20deep%20belief%20network%20algorithm%20for%20anomaly-based%20intrusion%20detection%20system.pdf
http://eprints.utem.edu.my/id/eprint/28241/2/Hybrid%20weight%20deep%20belief%20network%20algorithm%20for%20anomaly-based%20intrusion%20detection%20system.pdf
http://eprints.utem.edu.my/id/eprint/28241/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=123905
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spelling my.utem.eprints.282412024-11-12T10:35:37Z http://eprints.utem.edu.my/id/eprint/28241/ Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system Maseer, Ziadoon Kamil Q Science (General) With an increasing number of recent services connected to the Internet, including cloud computing and Internet of Things systems, cyber-attacks have become more challenging. The deep learning approach plays a pertinent role in tracing new attacks in cybersecurity. Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. However, the current DBN.NIDS model is still ineffective for large-scale real-world data due to some issues: 1) the pre-training of the DBN algorithm includes simple feature learning which does not work very well to extract important features from the attack data, 2) the classification task of the DBN algorithm is a poor detection for imbalanced class dataset and 3) the design of the DBN model could be weak and need to be continuously updated by modern definitions of abnormal to detect recent attacks. In this study, the Deep Belief Network algorithm was optimized and constructed to design an effective NIDS anomaly model. The optimized DBN algorithm, known as the HW-DBN algorithm, integrated through feature learning based on a Gaussian–Bernoulli Restricted Boltzmann Machine as well as classification task through a weight neuron network. The effectiveness of HW-DBN.NIDS was validated with real-world datasets that contained multiple attack types, complex data patterns, noise values, and imbalanced classes. A comparative analysis presented an HW-DBN.NIDS which was able to extract important features and detect the low frequency of modern attacks undetectable by other models. The results showed the proposed anomaly IDS model that outperformed the three models by achieving a higher recognition accuracy of 99.38%, 99.99%, and 1.00 for the Web, bot, and bot-IoT attacks in CICIDS2017 and CSE-CIC-IDS2018 dataset, respectively. In future, the HW-DBN algorithm can be proposed as an integrated deep Learning for the classification performance of attack detection models. 2022 Thesis NonPeerReviewed text en http://eprints.utem.edu.my/id/eprint/28241/1/Hybrid%20weight%20deep%20belief%20network%20algorithm%20for%20anomaly-based%20intrusion%20detection%20system.pdf text en http://eprints.utem.edu.my/id/eprint/28241/2/Hybrid%20weight%20deep%20belief%20network%20algorithm%20for%20anomaly-based%20intrusion%20detection%20system.pdf Maseer, Ziadoon Kamil (2022) Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system. Doctoral thesis, Universiti Teknikal Malaysia Melaka. https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=123905
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
topic Q Science (General)
spellingShingle Q Science (General)
Maseer, Ziadoon Kamil
Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
description With an increasing number of recent services connected to the Internet, including cloud computing and Internet of Things systems, cyber-attacks have become more challenging. The deep learning approach plays a pertinent role in tracing new attacks in cybersecurity. Recently, researchers suggested a deep belief network (DBN) algorithm to construct and build a network intrusion detection system (NIDS) for detecting attacks that have not been seen before. However, the current DBN.NIDS model is still ineffective for large-scale real-world data due to some issues: 1) the pre-training of the DBN algorithm includes simple feature learning which does not work very well to extract important features from the attack data, 2) the classification task of the DBN algorithm is a poor detection for imbalanced class dataset and 3) the design of the DBN model could be weak and need to be continuously updated by modern definitions of abnormal to detect recent attacks. In this study, the Deep Belief Network algorithm was optimized and constructed to design an effective NIDS anomaly model. The optimized DBN algorithm, known as the HW-DBN algorithm, integrated through feature learning based on a Gaussian–Bernoulli Restricted Boltzmann Machine as well as classification task through a weight neuron network. The effectiveness of HW-DBN.NIDS was validated with real-world datasets that contained multiple attack types, complex data patterns, noise values, and imbalanced classes. A comparative analysis presented an HW-DBN.NIDS which was able to extract important features and detect the low frequency of modern attacks undetectable by other models. The results showed the proposed anomaly IDS model that outperformed the three models by achieving a higher recognition accuracy of 99.38%, 99.99%, and 1.00 for the Web, bot, and bot-IoT attacks in CICIDS2017 and CSE-CIC-IDS2018 dataset, respectively. In future, the HW-DBN algorithm can be proposed as an integrated deep Learning for the classification performance of attack detection models.
format Thesis
author Maseer, Ziadoon Kamil
author_facet Maseer, Ziadoon Kamil
author_sort Maseer, Ziadoon Kamil
title Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
title_short Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
title_full Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
title_fullStr Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
title_full_unstemmed Hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
title_sort hybrid weight deep belief network algorithm for anomaly-based intrusion detection system
publishDate 2022
url http://eprints.utem.edu.my/id/eprint/28241/1/Hybrid%20weight%20deep%20belief%20network%20algorithm%20for%20anomaly-based%20intrusion%20detection%20system.pdf
http://eprints.utem.edu.my/id/eprint/28241/2/Hybrid%20weight%20deep%20belief%20network%20algorithm%20for%20anomaly-based%20intrusion%20detection%20system.pdf
http://eprints.utem.edu.my/id/eprint/28241/
https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=123905
_version_ 1817848425015672832
score 13.223943

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