The goal of this project is to provide neural network architectures for traffic classification and their pre-trained weights.
The package provides two network architectures, 30pktTCNET and Multi-modal CESNET v2, both visualized in the following pictures. See the getting started page and models reference for more information.
:frog: :frog: See a related project CESNET DataZoo providing large TLS and QUIC traffic datasets. :frog: :frog:
:notebook: :notebook: Example Jupyter notebooks are included in a separate CESNET Traffic Classification Examples repo. :notebook: :notebook:
30pktTCNET
Multi-modal CESNET v2
Installation
Install the package from pip with:
pip install cesnet-models
or for editable install with:
pip install -e git+https://github.com/CESNET/cesnet-models
Papers
Models from the following papers are included:
-
Universal Embedding Function for Traffic Classification via QUIC Domain Recognition Pretraining: A Transfer Learning Success
Jan Luxemburk, Karel Hynek, Richard Plný, and Tomáš Čejka
arXiv preprint, 2025 -
Encrypted traffic classification: the QUIC case
Jan Luxemburk and Karel Hynek
2023 7th Network Traffic Measurement and Analysis Conference (TMA) -
Fine-grained TLS services classification with reject option
Jan Luxemburk and Tomáš Čejka
Computer Networks, 2023
1 maintainer
Metadata
1 maintainer
Release files for cesnet-models 0.4.1
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
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| cesnet_models-0.4.1.tar.gz | 36.6 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| cesnet_models-0.4.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 75.2 kB
Release files / cesnet_models-0.4.1.tar.gz
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