  The rapid digital transformation without security considerations has resulted
in the rise of global-scale cyberattacks. The first line of defense against
these attacks are Network Intrusion Detection Systems (NIDS). Once deployed,
however, these systems work as blackboxes with a high rate of false positives
with no measurable effectiveness. There is a need to continuously test and
improve these systems by emulating real-world network attack mutations. We
present SynGAN, a framework that generates adversarial network attacks using
the Generative Adversial Networks (GAN). SynGAN generates malicious packet flow
mutations using real attack traffic, which can improve NIDS attack detection
rates. As a first step, we compare two public datasets, NSL-KDD and CICIDS2017,
for generating synthetic Distributed Denial of Service (DDoS) network attacks.
We evaluate the attack quality (real vs. synthetic) using a gradient boosting
classifier.
