Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System

Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. While having a precise classification model in separating the normal and malicious network traffics still remain as th...

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Bibliographic Details
Main Author:	Chew, Yee Jian
Format:	Thesis
Published:	2019
Subjects:	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
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id	my-mmu-ep.7752
record_format	uketd_dc
spelling	my-mmu-ep.77522020-09-21T20:52:35Z Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System 2019-07 Chew, Yee Jian TK5101-6720 Telecommunication. Including telegraphy, telephone, radio, radar, television Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. While having a precise classification model in separating the normal and malicious network traffics still remain as the ultimate goal, the privacy protection for network traffic database cannot be ignore as well. The common solution to tackle this matter is anonymising the database through the statistical approach. Anonymising can be referred to masking, hiding or removing certain sensitive information from the database. In the past decades, numerous anonymisation tools and techniques have been developed to conceal the sensitive information which could be revealed by the network data. The main usage of privacy solutions is to conceal the potentially sensitive information in the network traces. However, it is also important to ensure the anonymisation techniques are not severely deteriorating the performances of NIDS. Presently, the conventional way to gauge the usability of network data is by exploiting the number of alarms generated by Snort NIDS before-and-after an anonymisation solution. Nevertheless, this approach may not be feasible when considering the application of machine learning in segregating the traffics. In order to fill this gap, 10 notable machine classifiers are employed to evaluate the performances of 2 network data privacy solutions: (1) port number bilateral classification and (2) IP truncation. Utility of the network data is measured based on the classification accuracy attained. 2019-07 Thesis http://shdl.mmu.edu.my/7752/ http://library.mmu.edu.my/library2/diglib/mmuetd/ masters Multimedia University Faculty of Information Science & Technology
institution	Multimedia University
collection	MMU Institutional Repository
topic	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television
spellingShingle	TK5101-6720 Telecommunication Including telegraphy, telephone, radio, radar, television Chew, Yee Jian Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System
description	Machine learning techniques have been extensively adopted in the domain of Network-based Intrusion Detection System (NIDS) especially in the task of network traffics classification. While having a precise classification model in separating the normal and malicious network traffics still remain as the ultimate goal, the privacy protection for network traffic database cannot be ignore as well. The common solution to tackle this matter is anonymising the database through the statistical approach. Anonymising can be referred to masking, hiding or removing certain sensitive information from the database. In the past decades, numerous anonymisation tools and techniques have been developed to conceal the sensitive information which could be revealed by the network data. The main usage of privacy solutions is to conceal the potentially sensitive information in the network traces. However, it is also important to ensure the anonymisation techniques are not severely deteriorating the performances of NIDS. Presently, the conventional way to gauge the usability of network data is by exploiting the number of alarms generated by Snort NIDS before-and-after an anonymisation solution. Nevertheless, this approach may not be feasible when considering the application of machine learning in segregating the traffics. In order to fill this gap, 10 notable machine classifiers are employed to evaluate the performances of 2 network data privacy solutions: (1) port number bilateral classification and (2) IP truncation. Utility of the network data is measured based on the classification accuracy attained.
format	Thesis
qualification_level	Master's degree
author	Chew, Yee Jian
author_facet	Chew, Yee Jian
author_sort	Chew, Yee Jian
title	Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System
title_short	Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System
title_full	Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System
title_fullStr	Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System
title_full_unstemmed	Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System
title_sort	privacy-preserving decision tree pruning in network-based intrusion detection system
granting_institution	Multimedia University
granting_department	Faculty of Information Science & Technology
publishDate	2019
_version_	1747829674045603840

Privacy-Preserving Decision Tree Pruning In Network-Based Intrusion Detection System

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