| 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 | Support vector machines , Bandwidth , Switches , Denial-of-service attack , Ubiquitous computing , Control systems , Software |
| journal | 2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) |
| volume | Not Available |
| issue | Not Available |
| pages | Not Available |
| publisher | IEEE |
| Local/International | Local |
| Paper Link | https://ieeexplore.ieee.org/abstract/document/9447680 |
| 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, cost-efficient, 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.