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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.

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