The data folder contains the data to train the model on.
The scripts to train and evaluate a topic model can be found in lda.
Start the Docker container via lda/run.sh and afterwards start the
pipeline via scripts/run.sh.
The scripts to train and evaluate a neural network can be found in nn.
Start the Docker container via docker-compose up in the projects root
and afterwards a shell via docker exec -it jupyter_notebook sh. Then
start the pipeline via cd nn && sh ./run.sh.
Attention: The example datasets are too small, thus the NN pipeline will fail.
All code runs in Docker containers.
All components are licensed under GPLv3 License.
Model Generation for the CaseStudy IntrusionDetection
Copyright (C) 2021 CaseStudy IntrusionDetection Developers
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <https://www.gnu.org/licenses/>.
The structure was initially based on the cookiecutter data science structure.