Survey on DDoS Detection in Software Defined Network Using Machine Learning Techniques

Abstract

This paper is a survey on existing techniques of machine learning used for DDoS detection in software-defined networks. A software-defined network that has a centralized controller is prone to DDoS attacks and so a mechanism to detect it at earliest and perform mitigation is required. This paper describes the datasets used, the detection technique used, and a comparison of their results.