| publication name | SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network |
|---|---|
| Authors | Shimaa Ezzat Kotb, Heba .A Tag El-Dien, Adly S.Tag Eldien |
| year | 2021 |
| keywords | Software Defined Networking (SDN), SGuard, Distributed Denial o f Service attack (DDoS attack), Support Vector Machine (SVM). |
| journal | |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | Not Available |
| Local/International | International |
| Paper Link | Not Available |
| Full paper | download |
| Supplementary materials | Not Available |
Abstract
A Software Defined Networking (SDN) is an advanced network design that presents central control for a complete network. It is a dynamic, easy-to-manage, costefficient, and adaptive advanced architecture, making it utilitarian for dynamic nature and high-bandwidth of the present applications. Distributed Denial-of-Service (DDoS) attacks specific to SDN networks to deplete the control plane bandwidth and overload the buffer memory of OpenFlow switch. In this research, a design and implementation of secure guard to assist in solving the issue of DDoS attacks on pox controller is presented, this guard is named SGuard. A novel Five-tuple as feature vector is utilized for classifying traffic flow using Support Vector Machine (SVM). A Mininet is utilized to evaluate SGuard in a software environment. The introduced system is evaluated by measuring the system’s performance in terms of delay, bandwidth, traffic flow and accuracy