Ing. Matej Hulák

Ing. Matej Hulák

Mgr. Martin Jureček, Ph.D.

Department of Information Security

This bachelors thesis focuses on the classification of operating systems based on network flows. Machine learning methods are used for the classification. Before applying machine learning, relevant information is aggregated from the network flows to serve as input features. The solution is implemented in the Python programming language using the scikit-learn library and also includes analysis of the machine learning model's outputs. The best result was achieved using the Random Forest algorithm, which reached an 86,46 % F1-macro score on the test dataset. Real-world testing on production network data showed that even without some input features, the module is capable of classifying devices with an F1-macro score of 59,08 %. The results indicate potential for further improvement in classification accuracy and reliability. To enable the application of the classifier a module for the NEMEA system is developed, capable of performing the classification in real time.

Ing. Matej Hulák

Ing. Josef Koumar

Department of Information Security

The thesis aims to detect proxy servers, specifically the Residential Proxy servers (RESIP) in the network based on the analysis of network flows from the perspective of an Internet Service Provider (ISP). By applying machine learning techniques a functional module for the NEMEA system will be developed. This module detects RESIP in the network thereby enhancing network management and security.