Predict the future of your network using the best time series ML model that fit with your traffic.
- You can try with your own data builded from raw netflow.
- Fork this project to your Github account.
- This software is created under MIT License
Important
The following work and its results are the result of a project presented at the end of the subject of the pattern recognition class of the Master's in Science course Computing at the State University of Londrina (UEL) and does not have the objective of being published as a scientific article.
You can read the written paper here, please notice to the above advice.
We've some methods to get up and running the application:
Using pure Python
- Python v3.11.10.
git clone [email protected]:MuriloChianfa/network-traffic-time-series-forecasting.git
cd network-traffic-time-series-forecasting
virtualenv -p python3.11 venv
. ./venv/bin/activate
pip install -r requirements.txt
streamlit run forecasting/main.py
Using Docker Compose
- Docker v24.0 or higher.
- Docker Compose v2.13 or higher.
- Your may need nvidia-container-toolkit.
git clone [email protected]:MuriloChianfa/network-traffic-time-series-forecasting.git
cd network-traffic-time-series-forecasting
docker compose -f docker-compose.yml up -d
Building your own dataset
cd preprocess
go mod init network-traffic-time-series-forecasting
go mod tidy
go env -w GO111MODULE=on
go get github.com/phaag/go-nfdump@d2ff6042cb5186ede4064cbd50253ab97a78a89e
go run extract-traffic.go