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...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2018
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| Subjects: | |
| Online Access: | http://eprints.usm.my/60904/ http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf |
| _version_ | 1848884567879974912 |
|---|---|
| author | Aun, Yichiet |
| author_facet | Aun, Yichiet |
| author_sort | Aun, Yichiet |
| building | USM Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T19:08:46Z |
| format | Thesis |
| id | usm-60904 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:08:46Z |
| publishDate | 2018 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-609042024-08-09T01:51:58Z http://eprints.usm.my/60904/ Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications 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 NonPeerReviewed application/pdf en http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf Aun, Yichiet (2018) Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | R856-857 Biomedical engineering. Electronics. Instrumentation Aun, Yichiet Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications |
| title | 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_short | Behavioural Feature Extraction For
Context-Aware Traffic Classification
Of Mobile Applications |
| title_sort | behavioural feature extraction for
context-aware traffic classification
of mobile applications |
| topic | R856-857 Biomedical engineering. Electronics. Instrumentation |
| url | http://eprints.usm.my/60904/ http://eprints.usm.my/60904/1/Behavioural%20feature%20extraction%20for%20cut.pdf |