# A GAN-Based Framework for Robust DDoS Attack Detection

arXiv: https://arxiv.org/abs/2609.18281 · HTML: https://mlsanity.com/paper/2609.18281/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 16 Sep 2026.

## Builds on (most cited first, 5 of 5)

- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks](https://mlsanity.com/paper/2202.03277/) (2022)
- [Adversarial Challenges in Network Intrusion Detection Systems: Research Insights and Future Prospects](https://mlsanity.com/paper/2409.18736/) (2024)
- [GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection](https://mlsanity.com/paper/2201.13102/) (2022)
- [Anomaly-Flow: A Multi-domain Federated Generative Adversarial Network for Distributed Denial-of-Service Detection](https://mlsanity.com/paper/2503.14618/) (2025)

## Abstract (arXiv)

The availability and consistency of online services remain vulnerable due to Distributed Denial of Service (DDoS) attacks. These attacks are evolving by adopting more complex strategies to evade traditional network security systems. Despite the effectiveness of machine learning models in detecting DDoS traffic, targeted adversarial attacks can degrade their classification accuracy. This work proposes a robust detection framework that integrates generative adversarial modelling with advanced machine learning models. We trained Random Forests, Deep Neural Ensembles, and Transformer-based models using the CICDDoS2019 dataset to establish the frameworks baseline performance. To enhance the models defensive capacity, we generated synthetic adversarial flows that simulate potential evasion attempts and adversarial traffic using a Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP). Then, we combined the generated traffic with benign and malicious traffic to construct hybrid datasets to train the models to learn more generalizable decision boundaries. The experimental results indicate that the proposed methodology significantly enhances detection accuracy and resilience, especially against unseen adversarial traffic. We also tested the designed framework using real-world generated traffic, which demonstrates its capability in practical settings. The scalable and efficient solution against adversarial DDoS attacks, introduced in this work, paves the way towards more resilient and adaptive network defense systems that combine generative adversarial augmentation with recent advances in learning models.