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SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network

2021 International Mobile, Intelligent, and Ubiquitous Computing Conference (MIUCC) • 2021
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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.