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

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....

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Format:	Thesis
Language:	English
Subjects:	Telecommuncation > Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering
Online Access:	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/4/Alhamza%20Munther.pdf
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id	my-unimap-72698
record_format	uketd_dc
spelling	my-unimap-726982023-03-23T06:53:23Z An efficient network traffic classification based on vital random forest for high dimensional dataset Rozmie Razif, Othman, Dr. 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. Universiti Malaysia Perlis (UniMAP) Thesis en http://dspace.unimap.edu.my:80/xmlui/handle/123456789/72698 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/3/license.txt 8a4605be74aa9ea9d79846c1fba20a33 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/1/Page%201-24.pdf f130888c261fedc52162acd4b6be226b http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/2/Full%20text.pdf 46126ea5253920ac59362355317ca4f4 http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/4/Alhamza%20Munther.pdf 3338270378a7685126a37fa2cb979b20 Universiti Malaysia Perlis (UniMAP) Telecommuncation -- Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering School of Computer and Communication Engineering
institution	Universiti Malaysia Perlis
collection	UniMAP Institutional Repository
language	English
advisor	Rozmie Razif, Othman, Dr.
topic	Telecommuncation -- Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering
spellingShingle	Telecommuncation -- Traffic Computer networks Internetworking (Telecommunication) Network traffic engineering An efficient network traffic classification based on vital random forest for high dimensional dataset
description	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.
format	Thesis
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
granting_institution	Universiti Malaysia Perlis (UniMAP)
granting_department	School of Computer and Communication Engineering
url	http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/1/Page%201-24.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/2/Full%20text.pdf http://dspace.unimap.edu.my:80/xmlui/bitstream/123456789/72698/4/Alhamza%20Munther.pdf
_version_	1776104225611186176

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

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