# SynGAN: Towards Generating Synthetic Network Attacks using GANs

arXiv: https://arxiv.org/abs/1908.09899 · HTML: https://mlsanity.com/paper/1908.09899/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [FGAN: Federated Generative Adversarial Networks for Anomaly Detection in Network Traffic](https://mlsanity.com/paper/2203.11106/) (2022)
- [Adversarial Debiasing of Machine Learning Models for Enhanced Network Security against DDoS Attacks](https://mlsanity.com/paper/2609.36167/) (2026)

## Abstract (arXiv)

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.