# NAttack! Adversarial Attacks to bypass a GAN based classifier trained to detect Network intrusion

arXiv: https://arxiv.org/abs/2002.08527 · HTML: https://mlsanity.com/paper/2002.08527/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Feb 2020.

## Built on by (most cited first, 5 of 5)

- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Hybrid Deep Learning Model using SPCAGAN Augmentation for Insider Threat Analysis](https://mlsanity.com/paper/2203.02855/) (2022)
- [Information Stealing in Federated Learning Systems Based on Generative Adversarial Networks](https://mlsanity.com/paper/2108.00701/) (2021)
- [A Defensive Framework Against Adversarial Attacks on Machine Learning-Based Network Intrusion Detection Systems](https://mlsanity.com/paper/2502.15561/) (2025)
- [Generative Adversarial Network-Driven Detection of Adversarial Tasks in Mobile Crowdsensing](https://mlsanity.com/paper/2202.07802/) (2022)

## Abstract (arXiv)

With the recent developments in artificial intelligence and machine learning, anomalies in network traffic can be detected using machine learning approaches. Before the rise of machine learning, network anomalies which could imply an attack, were detected using well-crafted rules. An attacker who has knowledge in the field of cyber-defence could make educated guesses to sometimes accurately predict which particular features of network traffic data the cyber-defence mechanism is looking at. With this information, the attacker can circumvent a rule-based cyber-defense system. However, after the advancements of machine learning for network anomaly, it is not easy for a human to understand how to bypass a cyber-defence system. Recently, adversarial attacks have become increasingly common to defeat machine learning algorithms. In this paper, we show that even if we build a classifier and train it with adversarial examples for network data, we can use adversarial attacks and successfully break the system. We propose a Generative Adversarial Network(GAN)based algorithm to generate data to train an efficient neural network based classifier, and we subsequently break the system using adversarial attacks.