Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin

The "Remote to Local (R2L) Intrusion Detection System Using Decision Tree" project aims to address the escalating threat of network intrusions by developing an effective intrusion detection system. The background study emphasizes the increasing significance of network security and the prev...

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Main Author:	Mohd Nordin, Ahmad Nasreen Aqmal
Format:	Thesis
Language:	English
Published:	2024
Subjects:	Algorithms
Online Access:	https://ir.uitm.edu.my/id/eprint/95537/1/95537.pdf
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id	my-uitm-ir.95537
record_format	uketd_dc
spelling	my-uitm-ir.955372024-05-31T02:52:47Z Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin 2024 Mohd Nordin, Ahmad Nasreen Aqmal Algorithms The "Remote to Local (R2L) Intrusion Detection System Using Decision Tree" project aims to address the escalating threat of network intrusions by developing an effective intrusion detection system. The background study emphasizes the increasing significance of network security and the prevalence of various types of network intrusion attacks, particularly Remote to Local (R2L) attacks. The problem statement underscores the need for robust intrusion detection mechanisms to safeguard against unauthorized access and potential data breaches. The objectives of the project include conducting a comprehensive literature study, collecting relevant data, and preprocessing the dataset. The system design phase encompasses the development of system architecture, flowcharts, pseudocode, and interface design. The implementation phase focuses on the deployment of the Decision Tree algorithm and system evaluation through functionality testing. The key results encompass dataset preprocessing, Decision Tree classification model training, user interface development, and the evaluation of the Decision Tree model's performance. The project successfully achieves its predetermined objectives, culminating in the development of an effective Remote to Local (R2L) Intrusion Detection System utilizing the Decision Tree algorithm. The trained model achieved commendable test accuracy of 97.26% while maintaining a low false alarm rate and miss rate, scoring approximately 3.61% and 2.19% respectively, this result ensuring a robust and efficient approach to R2L intrusion detection. The project also acknowledges its limitations and provides recommendations for future work, emphasizing the potential for further enhancements in subsequent revisions. This endeavor serves as a crucial initial step towards fortifying network security and mitigating the risks associated with network intrusion attacks. 2024 Thesis https://ir.uitm.edu.my/id/eprint/95537/ https://ir.uitm.edu.my/id/eprint/95537/1/95537.pdf text en public degree Universiti Teknologi MARA, Terengganu College of Computing, Informatics and Media Ismail, Najiahtul Syafiqah
institution	Universiti Teknologi MARA
collection	UiTM Institutional Repository
language	English
advisor	Ismail, Najiahtul Syafiqah
topic	Algorithms
spellingShingle	Algorithms Mohd Nordin, Ahmad Nasreen Aqmal Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
description	The "Remote to Local (R2L) Intrusion Detection System Using Decision Tree" project aims to address the escalating threat of network intrusions by developing an effective intrusion detection system. The background study emphasizes the increasing significance of network security and the prevalence of various types of network intrusion attacks, particularly Remote to Local (R2L) attacks. The problem statement underscores the need for robust intrusion detection mechanisms to safeguard against unauthorized access and potential data breaches. The objectives of the project include conducting a comprehensive literature study, collecting relevant data, and preprocessing the dataset. The system design phase encompasses the development of system architecture, flowcharts, pseudocode, and interface design. The implementation phase focuses on the deployment of the Decision Tree algorithm and system evaluation through functionality testing. The key results encompass dataset preprocessing, Decision Tree classification model training, user interface development, and the evaluation of the Decision Tree model's performance. The project successfully achieves its predetermined objectives, culminating in the development of an effective Remote to Local (R2L) Intrusion Detection System utilizing the Decision Tree algorithm. The trained model achieved commendable test accuracy of 97.26% while maintaining a low false alarm rate and miss rate, scoring approximately 3.61% and 2.19% respectively, this result ensuring a robust and efficient approach to R2L intrusion detection. The project also acknowledges its limitations and provides recommendations for future work, emphasizing the potential for further enhancements in subsequent revisions. This endeavor serves as a crucial initial step towards fortifying network security and mitigating the risks associated with network intrusion attacks.
format	Thesis
qualification_level	Bachelor degree
author	Mohd Nordin, Ahmad Nasreen Aqmal
author_facet	Mohd Nordin, Ahmad Nasreen Aqmal
author_sort	Mohd Nordin, Ahmad Nasreen Aqmal
title	Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
title_short	Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
title_full	Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
title_fullStr	Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
title_full_unstemmed	Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin
title_sort	detecting remote-to-local (r2l) attack using decision tree algorithm / ahmad nasreen aqmal mohd nordin
granting_institution	Universiti Teknologi MARA, Terengganu
granting_department	College of Computing, Informatics and Media
publishDate	2024
url	https://ir.uitm.edu.my/id/eprint/95537/1/95537.pdf
_version_	1804889963428315136

Detecting Remote-To-Local (R2L) attack using Decision Tree algorithm / Ahmad Nasreen Aqmal Mohd Nordin

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