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

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dc.contributor.authorAun, Yichiet
dc.date.accessioned2024-08-09T01:49:04Z
dc.date.available2024-08-09T01:49:04Z
dc.date.issued2018-06
dc.description.abstractTraffic 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.
dc.identifier.urihttps://erepo.usm.my/handle/123456789/20157
dc.language.isoen
dc.subjectContext-Aware Traffic
dc.subjectMobile Applications
dc.titleBehavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications
dc.typeResource Types::text::thesis::doctoral thesis
dspace.entity.typePublication
oairecerif.author.affiliationUniversiti Sains Malaysia
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