# A Deep Learning Based DDoS Detection System in Software-Defined Networking (SDN)

arXiv: https://arxiv.org/abs/1611.07400 · HTML: https://mlsanity.com/paper/1611.07400/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Nov 2016.

## 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)
- [DoS and DDoS Mitigation Using Variational Autoencoders](https://mlsanity.com/paper/2105.06899/) (2021)
- [Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network](https://mlsanity.com/paper/2102.01873/) (2021)
- [A Review of the Convergence of 5G/6G Architecture and Deep Learning](https://mlsanity.com/paper/2208.07643/) (2022)
- [Insights on Adversarial Attacks for Tabular Machine Learning via a Systematic Literature Review](https://mlsanity.com/paper/2506.15506/) (2025)

## Abstract (arXiv)

Distributed Denial of Service (DDoS) is one of the most prevalent attacks that an organizational network infrastructure comes across nowadays. We propose a deep learning based multi-vector DDoS detection system in a software-defined network (SDN) environment. SDN provides flexibility to program network devices for different objectives and eliminates the need for third-party vendor-specific hardware. We implement our system as a network application on top of an SDN controller. We use deep learning for feature reduction of a large set of features derived from network traffic headers. We evaluate our system based on different performance metrics by applying it on traffic traces collected from different scenarios. We observe high accuracy with a low false-positive for attack detection in our proposed system.