<p align="center">
<img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/models.svg" width="450">
</p>
[](https://github.com/CESNET/cesnet-models/blob/main/LICENCE)
[](https://cesnet.github.io/cesnet-models/)
[](https://pypi.org/project/cesnet-models/)
[](https://pypi.org/project/cesnet-models/)
The goal of this project is to provide neural network architectures for traffic classification and their pre-trained weights.
The newest network architecture is named Multi-modal CESNET v2 (mm-CESNET-v2) and is visualized in the following picture. See the [getting started](https://cesnet.github.io/cesnet-models/getting_started/) page and [models](https://cesnet.github.io/cesnet-models/reference_models/) reference for more information.
:frog: :frog: See a related project [CESNET DataZoo](https://github.com/CESNET/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](https://github.com/CESNET/cesnet-tcexamples) repo. :notebook: :notebook:
### Multi-modal CESNET v2
<p align="center">
<img src="https://raw.githubusercontent.com/CESNET/cesnet-models/main/docs/images/model-mm-cesnet-v2.png" width="450">
</p>
## Installation
Install the package from pip with:
```bash
pip install cesnet-models
```
or for editable install with:
```bash
pip install -e git+https://github.com/CESNET/cesnet-models
```
## Papers
Models from the following papers are included:
* [Fine-grained TLS services classification with reject option](https://doi.org/10.1016/j.comnet.2022.109467) <br>
Jan Luxemburk and Tomáš Čejka <br>
Computer Networks, 2023
* [Encrypted traffic classification: the QUIC case](https://doi.org/10.23919/TMA58422.2023.10199052) <br>
Jan Luxemburk and Karel Hynek <br>
2023 7th Network Traffic Measurement and Analysis Conference (TMA)
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