Early Detection of DDoS Attacks in SDN using Machine Learning Models

Abstract

Software Defined Networks (SDN) are programmable networks that can be easily managed with a global understanding of network topology. However, while the software-defined network architecture enhances network resource pooling by separating the control layer from the data layer, this centralized management and control introduces security vulnerabilities into the SDN architecture. One of the most dangerous attacks that the SDN architecture faces is distributed denial of service (DDoS). Aiming at the detection of DDoS attacks under the SDN architecture, this paper proposes faster DDoS attack detection using machine learning based classifier XGBoost which provides higher accuracy