WCGAN-GP

Wasserstein Conditional GAN with Gradient Penalty or WCGAN-GP for short, is a Generative Adversarial Network model used by Walia, Tierney and McKeever 2020 to create synthetic tabular data.

WCGAN-GP uses Wasserstein loss to overcome mode collapse, gradient penalty instead of weight clipping to increase stability in training, while also being a conditional GAN meaning that it can create data conditioned by input label.

This repo contains the TensorFlow 2 implementation of WCGAN-GP and demonstrates its use by creating synthetic data from CIC-IDS-2017 dataset.

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Install

https://github.com/marzekan/WCGAN-GP.git

cd WCGAN-GP

pip install -r requirements.txt

or

pipenv shell

Run

Checkout the Jupyter Notebook

pipenv run juypter notebook

... and open wcgan-gp.ipynb

or

use the packaged module: wcgan.py

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Synthetic data evaluation results

Synthetic data evaluation was done using the TableEvaluator package.

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Important!

For demo purposes and to reduce resource usage the original CIC-IDS-2017 is sampled to 25% of the original dataset size.

This is sure to implact the results so if you want to get the best possible results you should train the WCGAN-GP on entire CIC-IDS-2017 after running it through cleaning-cic-ids-2017.ipynb and data-preproc.ipynb notebooks.

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Sources

Walia, Manhar & Tierney, Brendan & Mckeever, Susan. (2020). Synthesising Tabular Data using Wasserstein Conditional GANs with Gradient Penalty (WCGAN-GP)

Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017). Improved Training of Wasserstein GANs.

https://github.com/eriklindernoren/Keras-GAN