You are in:Home/Publications/SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network

Dr. Heba Allah Adly Tag El-Dien :: Publications:

Title:
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: Not Available
Volume: Not Available
Issue: Not Available
Pages: Not Available
Publisher: Not Available
Local/International: International
Paper Link: Not Available
Full paper Heba Allah Adly Tag El-Dien_SGuard machine learning-based Distrbuted.pdf
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

Google ScholarAcdemia.eduResearch GateLinkedinFacebookTwitterGoogle PlusYoutubeWordpressInstagramMendeleyZoteroEvernoteORCIDScopus