Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications

Traffic classification is becoming more complex due to proliferations of mobile applications coupled with growing diversity of traffic classes. This motivates the needs for improved traffic classification method that preserve classification accuracy while supporting more traffic classes. This the...

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Bibliographic Details
Main Author:	Aun, Yichiet
Format:	Thesis
Language:	English
Published:	2018
Subjects:	R856-857 Biomedical engineering Electronics Instrumentation
Online Access:	http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf
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id	my-usm-ep.60904
record_format	uketd_dc
spelling	my-usm-ep.609042024-08-09T01:51:58Z Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications 2018-06 Aun, Yichiet R856-857 Biomedical engineering. Electronics. Instrumentation Traffic classification is becoming more complex due to proliferations of mobile applications coupled with growing diversity of traffic classes. This motivates the needs for improved traffic classification method that preserve classification accuracy while supporting more traffic classes. This thesis identified domain-specific features that are effective for accurate, large-scale and scalable mobile applications classification using machine learning techniques. This thesis designed a context-aware traffic classification framework that includes a set of sequential algorithms from cleaning datasets, to identifying new features and detecting optimal classifier(s) based on problem contexts to improve classification accuracy in multi-variate traffic classification. 2018-06 Thesis http://eprints.usm.my/60904/ http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf application/pdf en public phd doctoral Universiti Sains Malaysia Pusat IPv6 Termaju Negara
institution	Universiti Sains Malaysia
collection	USM Institutional Repository
language	English
topic	R856-857 Biomedical engineering Electronics Instrumentation
spellingShingle	R856-857 Biomedical engineering Electronics Instrumentation Aun, Yichiet Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
description	Traffic classification is becoming more complex due to proliferations of mobile applications coupled with growing diversity of traffic classes. This motivates the needs for improved traffic classification method that preserve classification accuracy while supporting more traffic classes. This thesis identified domain-specific features that are effective for accurate, large-scale and scalable mobile applications classification using machine learning techniques. This thesis designed a context-aware traffic classification framework that includes a set of sequential algorithms from cleaning datasets, to identifying new features and detecting optimal classifier(s) based on problem contexts to improve classification accuracy in multi-variate traffic classification.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Aun, Yichiet
author_facet	Aun, Yichiet
author_sort	Aun, Yichiet
title	Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
title_short	Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
title_full	Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
title_fullStr	Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
title_full_unstemmed	Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
title_sort	behavioural feature extraction for context-aware traffic classification of mobile applications
granting_institution	Universiti Sains Malaysia
granting_department	Pusat IPv6 Termaju Negara
publishDate	2018
url	http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf
_version_	1811772858629619712

Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications

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