SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network
2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) • 2021
Publication Information
Authors
Shimaa Ezzat Kotb; Heba.A Tag El-Dien; Adly S.Tag Eldien
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)
Publisher
IEEE
Volume
Not Available
Issue
Not Available
Pages
Not Available
publication.type
Local
Paper Link
Open Link
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.
Staff Members - Benha University