Staff Members - Benha University
SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network
• 2021
Publication Information
Authors
Shimaa Ezzat Kotb, Heba .A Tag El-Dien, Adly S.Tag Eldien
Keywords
Software Defined Networking (SDN), SGuard,
Distributed Denial o f Service attack (DDoS attack), Support
Vector Machine (SVM).
Journal
Not Available
Publisher
Not Available
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
International
Paper Link
Not Available
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
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