SGuard: machine learning-based Distrbuted Denial-of-Service Detection Scheme for Software Defined Network
• 2021
معلومات البحث
المؤلفون
Shimaa Ezzat Kotb, Heba .A Tag El-Dien, Adly S.Tag Eldien
الكلمات المفتاحية
Software Defined Networking (SDN), SGuard,
Distributed Denial o f Service attack (DDoS attack), Support
Vector Machine (SVM).
المجلة العلمية
Not Available
الناشر
Not Available
المجلد
Not Available
العدد
Not Available
الصفحات
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publication.type
International
رابط البحث
Not Available
المواد المرفقة
Not Available
الملخص
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
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