# Detecting and Mitigating DDoS Attacks with AI: A Survey

arXiv: https://arxiv.org/abs/2503.17867 · HTML: https://mlsanity.com/paper/2503.17867/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 120 references that are in the map.
- First posted on arXiv 22 Mar 2025.

## Builds on (most cited first, 20 of 20)

- [Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling](https://mlsanity.com/paper/1412.3555/) (2014)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Modeling Tabular data using Conditional GAN](https://mlsanity.com/paper/1907.00503/) (2019)
- [Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey](https://mlsanity.com/paper/2403.14608/) (2024)
- [EAD: Elastic-Net Attacks to Deep Neural Networks via Adversarial Examples](https://mlsanity.com/paper/1709.04114/) (2017)
- [Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection](https://mlsanity.com/paper/1802.09089/) (2018)
- [Towards the Development of Realistic Botnet Dataset in the Internet of Things for Network Forensic Analytics: Bot-IoT Dataset](https://mlsanity.com/paper/1811.00701/) (2018)
- [Deep Isolation Forest for Anomaly Detection](https://mlsanity.com/paper/2206.06602/) (2022)
- [Towards Lifelong Learning of Large Language Models: A Survey](https://mlsanity.com/paper/2406.06391/) (2024)
- [Towards Better Chain-of-Thought Prompting Strategies: A Survey](https://mlsanity.com/paper/2310.04959/) (2023)
- [k-Nearest Neighbour Classifiers: 2nd Edition (with Python examples)](https://mlsanity.com/paper/2004.04523/) (2020)
- [Stochastic Sign Descent Methods: New Algorithms and Better Theory](https://mlsanity.com/paper/1905.12938/) (2019)
- [Maximal Jacobian-based Saliency Map Attack](https://mlsanity.com/paper/1808.07945/) (2018)
- ["We Need Structured Output": Towards User-centered Constraints on Large Language Model Output](https://mlsanity.com/paper/2404.07362/) (2024)
- [Factor Analysis, Probabilistic Principal Component Analysis, Variational Inference, and Variational Autoencoder: Tutorial and Survey](https://mlsanity.com/paper/2101.00734/) (2021)
- [LUCID: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection](https://mlsanity.com/paper/2002.04902/) (2020)
- [Adversarial Machine Learning In Network Intrusion Detection Domain: A Systematic Review](https://mlsanity.com/paper/2112.03315/) (2021)
- [Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems](https://mlsanity.com/paper/2008.03677/) (2020)
- [GADoT: GAN-based Adversarial Training for Robust DDoS Attack Detection](https://mlsanity.com/paper/2201.13102/) (2022)
- [A Deep Learning Approach to Create DNS Amplification Attacks](https://mlsanity.com/paper/2206.14346/) (2022)

## Built on by (most cited first, 1 of 1)

- [Web Technologies Security in the AI Era: A Survey of CDN-Enhanced Defenses](https://mlsanity.com/paper/2512.06390/) (2025)

## Abstract (arXiv)

Distributed Denial of Service attacks represent an active cybersecurity research problem. Recent research shifted from static rule-based defenses towards AI-based detection and mitigation. This comprehensive survey covers several key topics. Preeminently, state-of-the-art AI detection methods are discussed. An in-depth taxonomy based on manual expert hierarchies and an AI-generated dendrogram are provided, thus settling DDoS categorization ambiguities. An important discussion on available datasets follows, covering data format options and their role in training AI detection methods together with adversarial training and examples augmentation. Beyond detection, AI based mitigation techniques are surveyed as well. Finally, multiple open research directions are proposed.