An efficient network traffic classification based on vital random forest for high dimensional dataset

Doctor of Philosophy in Computer Engineering

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Bibliographic Details
Main Author:	Alhamza Munther Wardi, Alalousi
Other Authors:	Rozmie Razif, Othman, Dr.
Format:	Thesis
Language:	English
Published:	Universiti Malaysia Perlis (UniMAP) 2017
Subjects:	Telecommuncation > Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering
Online Access:	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72698
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Institution:	Universiti Malaysia Perlis
Language:	English

id	my.unimap-72698
record_format	dspace
spelling	my.unimap-726982023-03-23T06:53:23Z An efficient network traffic classification based on vital random forest for high dimensional dataset Alhamza Munther Wardi, Alalousi Rozmie Razif, Othman, Dr. Telecommuncation -- Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering Doctor of Philosophy in Computer Engineering This thesis proposes and implements an efficient network traffic classification method based on a new vital random forest for high dimensional data. Network traffic engineering is one of the most important technologies that have witnessed a rapid growth in the revolution of worldwide technologies. Network traffic classification has added considerable interest as an important network engineering tool for network security, network design, as well as network monitoring and management. It can introduce different services such as identifying the applications which are most consuming for network resources, it represents the core part of automated intrusion detection systems, it helps to detect anomaly applications and it helps to know the widely-used applications for the intention of offering new products. On the other hand, several challenges faced by network engineers on their course to classify traffic. The most common of which are increasing application types and the huge size of data traffics. Therefore, many researchers have been competing in literature to introduce an efficient method for traffic classification. The efficiency is dependent on important factors such as classification accuracy, memory consumption and processing time. This thesis presents a Vital Random Forest (VRF) as efficient network traffic classification which is a one package that introduces a new features-selection technique, data inputs reduction and a new build model for original random forest method to classify network traffic for huge datasets. VRF aims to reduce processing time, increase classification accuracy and decrease memory consumption. 2017 2021-11-03T03:40:45Z 2021-11-03T03:40:45Z Thesis http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72698 en Universiti Malaysia Perlis (UniMAP) Universiti Malaysia Perlis (UniMAP) School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
building	UniMAP Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Malaysia Perlis
content_source	UniMAP Library Digital Repository
url_provider	http://dspace.unimap.edu.my/
language	English
topic	Telecommuncation -- Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering
spellingShingle	Telecommuncation -- Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering Alhamza Munther Wardi, Alalousi An efficient network traffic classification based on vital random forest for high dimensional dataset
description	Doctor of Philosophy in Computer Engineering
author2	Rozmie Razif, Othman, Dr.
author_facet	Rozmie Razif, Othman, Dr. Alhamza Munther Wardi, Alalousi
format	Thesis
author	Alhamza Munther Wardi, Alalousi
author_sort	Alhamza Munther Wardi, Alalousi
title	An efficient network traffic classification based on vital random forest for high dimensional dataset
title_short	An efficient network traffic classification based on vital random forest for high dimensional dataset
title_full	An efficient network traffic classification based on vital random forest for high dimensional dataset
title_fullStr	An efficient network traffic classification based on vital random forest for high dimensional dataset
title_full_unstemmed	An efficient network traffic classification based on vital random forest for high dimensional dataset
title_sort	efficient network traffic classification based on vital random forest for high dimensional dataset
publisher	Universiti Malaysia Perlis (UniMAP)
publishDate	2017
url	http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72698
_version_	1772813061815533568

An efficient network traffic classification based on vital random forest for high dimensional dataset

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