|Department:||School of Information Technology|
|Keywords:||Network traffic classification; Automation of classifiers; Traffic classifiers; Network traffic protocols; Traffic clustering; Payload-based traffic classifiers.|
|Full text PDF:||http://hdl.handle.net/10536/DRO/DU:30063015|
The thesis addresses a number of critical problems in regard to fully automating the process of network traffic classification and protocol identification. Several effective solutions based on statistical analysis and machine learning techniques are proposed, which significantly reduce the requirements for human interventions in network traffic classification systems.