Detection of TCP, UDP and ICMP DDOS attacks in SDN Using Machine Learning approach

Abstract

Software Defined Networking (SDN) is an architecture for the network to control centrally through programmed software applications. SDN enables the programming behaviour of the network centrally over software programs with the help of open Application interfaces. We can change the parameters of network connections in a dynamic manner. In conventional network, it is impossible to change the settings in a dynamic way, as it is a fixed connection. In SDN, the Control plane is controlled by software is at the center which links the application layer and infrastructure layer. Software-Defined Networking (SDN) has proved its efficiency in countering attacks by providing network surveillance and online configuration of the network. DDoS attack is a malicious attack in which the attacker floods the target server from various sources. This project uses SDN intrusion dataset for training. This project focuses on the implementation of a compatible way for finding the DDoS detriment in SDN employing multiple Machine Learning (ML) approaches.