Performance evaluation for different intrusion detection system algorithms using machine learning

Intrusion is a set of operations that decide to compromise the integrity, confidentiality, and convenience of pc resources. This definition ignores the success or failure of those operations, therefore it additionally corresponds to attacks on pc systems. The distinguishing actions that mitigate the...

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Main Author:	Zarir, Mustafa Nadhim
Format:	Thesis
Language:	English English English
Published:	2018
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
Online Access:	http://eprints.uthm.edu.my/434/1/24p%20MUSTAFA%20NADHIM%20ZARIR.pdf http://eprints.uthm.edu.my/434/2/MUSTAFA%20NADHIM%20ZARIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/434/3/MUSTAFA%20NADHIM%20ZARIR%20WATERMARK.pdf
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spelling	my-uthm-ep.4342021-07-25T06:22:10Z Performance evaluation for different intrusion detection system algorithms using machine learning 2018-12 Zarir, Mustafa Nadhim TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Intrusion is a set of operations that decide to compromise the integrity, confidentiality, and convenience of pc resources. This definition ignores the success or failure of those operations, therefore it additionally corresponds to attacks on pc systems. The distinguishing actions that mitigate the operations to compromise the integrity, confidentiality, or convenience of pc resources is termed as intrusion detection. The aim of the Intrusion Detection System (IDS) is to monitor the network system for any sort of attacks. The objectives of this project is to evaluate the performance of various intrusion detection algorithms based on machine learning. The algorithms considered are the Naive Bays Algorithm, Decision Tree Algorithm and Hybrid Algorithm for different datasets. The evaluation of of various intrusion detection algorithms for different datasets is done utilizing a set of performance metrics, including accuracy, precision, central processing unit (CPU) efficiency and execution time. The simulation results showed that the Decision Tree Algorithm achieved the highest precision rate by 95.5% within 42 seconds. For accuracy, it achieved 69.5% and can be considered good, as compared to all other algorithms. Additionally, the Decision Tree Algorithm records 22.5% in CPU utilization. For Naive Byes Algorithm it scored 90% for Precision, 65% for Accuracy and 14% for CPU Utilization. Lastly, for Algorithm, it obtained 91% for Precision76% for Accuracy and 23% for CPU. 2018-12 Thesis http://eprints.uthm.edu.my/434/ http://eprints.uthm.edu.my/434/1/24p%20MUSTAFA%20NADHIM%20ZARIR.pdf text en public http://eprints.uthm.edu.my/434/2/MUSTAFA%20NADHIM%20ZARIR%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/434/3/MUSTAFA%20NADHIM%20ZARIR%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Onn Malaysia Faculty of Electrical and Electronic 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 Zarir, Mustafa Nadhim Performance evaluation for different intrusion detection system algorithms using machine learning
description	Intrusion is a set of operations that decide to compromise the integrity, confidentiality, and convenience of pc resources. This definition ignores the success or failure of those operations, therefore it additionally corresponds to attacks on pc systems. The distinguishing actions that mitigate the operations to compromise the integrity, confidentiality, or convenience of pc resources is termed as intrusion detection. The aim of the Intrusion Detection System (IDS) is to monitor the network system for any sort of attacks. The objectives of this project is to evaluate the performance of various intrusion detection algorithms based on machine learning. The algorithms considered are the Naive Bays Algorithm, Decision Tree Algorithm and Hybrid Algorithm for different datasets. The evaluation of of various intrusion detection algorithms for different datasets is done utilizing a set of performance metrics, including accuracy, precision, central processing unit (CPU) efficiency and execution time. The simulation results showed that the Decision Tree Algorithm achieved the highest precision rate by 95.5% within 42 seconds. For accuracy, it achieved 69.5% and can be considered good, as compared to all other algorithms. Additionally, the Decision Tree Algorithm records 22.5% in CPU utilization. For Naive Byes Algorithm it scored 90% for Precision, 65% for Accuracy and 14% for CPU Utilization. Lastly, for Algorithm, it obtained 91% for Precision76% for Accuracy and 23% for CPU.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Zarir, Mustafa Nadhim
author_facet	Zarir, Mustafa Nadhim
author_sort	Zarir, Mustafa Nadhim
title	Performance evaluation for different intrusion detection system algorithms using machine learning
title_short	Performance evaluation for different intrusion detection system algorithms using machine learning
title_full	Performance evaluation for different intrusion detection system algorithms using machine learning
title_fullStr	Performance evaluation for different intrusion detection system algorithms using machine learning
title_full_unstemmed	Performance evaluation for different intrusion detection system algorithms using machine learning
title_sort	performance evaluation for different intrusion detection system algorithms using machine learning
granting_institution	Universiti Tun Hussein Onn Malaysia
granting_department	Faculty of Electrical and Electronic Engineering
publishDate	2018
url	http://eprints.uthm.edu.my/434/1/24p%20MUSTAFA%20NADHIM%20ZARIR.pdf http://eprints.uthm.edu.my/434/2/MUSTAFA%20NADHIM%20ZARIR%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/434/3/MUSTAFA%20NADHIM%20ZARIR%20WATERMARK.pdf
_version_	1747830609153097728

Performance evaluation for different intrusion detection system algorithms using machine learning

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