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

By Quamar Niyaz, Weiqing Sun, Ahmad Y. Javaid. EAI Endorsed Trans. Security Safety, 2016

304 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/8ca2551c96a79266fca8d6457efd09f9c20e9fbd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Aunn Raza
2. Tao Huang
3. Ala Al-Fuqaha
4. Hunain Arif
5. Xiuzhen Cheng
6. Amir Hussain
7. Renchao Xie
8. Wei Peng

In Computer Networks and Communications

1. Tao Huang
2. Ala Al-Fuqaha
3. Xiuzhen Cheng
4. Renchao Xie
5. Wei Peng
6. Jun-feng Xie
7. F. Yu
8. Junaid Qadir

Citations in the corpus

• Survey on SDN based network intrusion detection system using machine learning approaches (2018, methodology): “[8] Used stack auto-encoder, deep learning for feature reduction DDoS Detection System Can detect any DDoS attack, but has a Controller bottleneck in a vast network.”
• Edge Computing Security: State of the Art and Challenges (2019, methodology): “[37] used neural networks to identify DDoS attacks in software-defined networks.”
• A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges (2019, methodology): “In [212], an SDN-based DDoS detection system is proposed.”
• A Deep Learning Approach to Network Intrusion Detection (2018, background): “In [26], the authors claim their 5-class classification of the NSL-KDD dataset produced an f-score of 75.”
• Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges (2017, methodology): “Reference [255] proposed a DDoS detection system in SDN where dimensionality reduction is used for”