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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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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
score 13.222552

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