  Due to the growing rise of cyber attacks in the Internet, flow-based data
sets are crucial to increase the performance of the Machine Learning (ML)
components that run in network-based intrusion detection systems (IDS). To
overcome the existing network traffic data shortage in attack analysis, recent
works propose Generative Adversarial Networks (GANs) for synthetic flow-based
network traffic generation. Data privacy is appearing more and more as a strong
requirement when processing such network data, which suggests to find solutions
where synthetic data can fully replace real data. Because of the
ill-convergence of the GAN training, none of the existing solutions can
generate high-quality fully synthetic data that can totally substitute real
data in the training of IDS ML components. Therefore, they mix real with
synthetic data, which acts only as data augmentation components, leading to
privacy breaches as real data is used. In sharp contrast, in this work we
propose a novel deterministic way to measure the quality of the synthetic data
produced by a GAN both with respect to the real data and to its performance
when used for ML tasks. As a byproduct, we present a heuristic that uses these
metrics for selecting the best performing generator during GAN training,
leading to a stopping criterion. An additional heuristic is proposed to select
the best performing GANs when different types of synthetic data are to be used
in the same ML task. We demonstrate the adequacy of our proposal by generating
synthetic cryptomining attack traffic and normal traffic flow-based data using
an enhanced version of a Wasserstein GAN. We show that the generated synthetic
network traffic can completely replace real data when training a ML-based
cryptomining detector, obtaining similar performance and avoiding privacy
violations, since real data is not used in the training of the ML-based
detector.
