CESNET Models

This is documentation of the CESNET Models project.

The goal of this project is to provide neural network architectures for traffic classification and their pre-trained weights. The weights were trained using public datasets available in the CESNET DataZoo package.

The newest network architecture is named Multi-modal CESNET v2 (mm-CESNET-v2) and is visualized in the following picture. See the getting started page and models reference for more information.

Multi-modal CESNET v2

Papers

Models from the following papers are included:

• Fine-grained TLS services classification with reject option
  Jan Luxemburk and Tomáš Čejka
  Computer Networks, 2023

• Encrypted traffic classification: the QUIC case
  Jan Luxemburk and Karel Hynek
  2023 7th Network Traffic Measurement and Analysis Conference (TMA)