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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| 格式: | Thesis |
| 語言: | English |
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2018
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| 在線閱讀: | http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf |
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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 |