Network traffic classification has become an important research problem in network security since it has many practical applications. The thesis will describe and compare various approaches to the network traffic classification problem. The goal of the thesis is to propose and implement a framework for developing traffic classifiers that should achieve high performance in both speed and memory consumption while retaining high precision. Moreover, the classifiers for real world application protocols will be developed using the framework. The framework will be evaluated by an experiment with a real traffic and the results will be discussed with the results from literature.

Základní literatura:
[1] Erik Hjelmvik and Wolfgang John. Statistical protocol identification with spid: Preliminary results. In Swedish National Computer NetworkingWorkshop, 2009.
[2] Peter Dorfinger, Georg Panholzer and Wolfgang John. Entropy Estimation for Real-Time Encrypted Traffic Identification. In Traffic Monitoring and Analysis 2011. LNCS 6613, Springer. 2011.
[3] G. Szabó, J. Szule, Z. Turányi, and G. Pongrácz. Multi-level machine learning traffic classification system. In ICN 2012, The Eleventh International Conference on Networks, 2012.