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

Aun, Yichiet (2018) Behavioural Feature Extraction For Context-Aware Traffic Classification Of Mobile Applications. PhD thesis, Universiti Sains Malaysia.

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Abstract

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.

Item Type: Thesis (PhD)
Subjects: R Medicine > R Medicine (General) > R856-857 Biomedical engineering. Electronics. Instrumentation
Divisions: Pusat IPv6 Termaju Negara (National Advanced IPv6 Centre of Excellence NAv6) > Thesis
Depositing User: Mr Mohammad Harish Sabri
Date Deposited: 09 Aug 2024 01:51
Last Modified: 09 Aug 2024 01:51
URI: http://eprints.usm.my/id/eprint/60904

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