Abstract:
Modern Machine learning and deep learning research have shown massive advancements in
the area of generative models. The most popular framework of generative models is known as
Generative Adversarial Networks or GANS. Although the primary research on GANS was based
on computer vision problems, recent literature has shown a diverse range of GANS applications
including but not limited to text, audio, video etc. Currently, GANS is attracting the attention
of networking experts to achieve certain security goals. Till date, many different variants of
GANS have been proposed, making it difficult to choose the right variant of GANS, especially
for networks as this area is not studied deeply. This thesis aims to provide an in-depth analysis of
CycleGAN, Tabular GAN and Wasserstein GAN on different networking datasets to judge their
ability to generate synthetic network data. The different variants are evaluated on 3 Machine
Learning classifiers (i.e., Decision Trees, Random Forest, and XGBoost) by comparing their
results with original datasets. These results and analysis will help network researchers and
professionals to decide the best variant of GANs for their data. Furthermore, our work will help
the network analysts by providing synthetic labelled data since data collection and labelling is
also a challenge in networking.
