Comparative Study on IDS using Machine learning approaches for Software Defined Networks

Abstract

Software defined networking (SDN) is an emerging network approach that separates the data plane from control plane and enables programmable features to efficiently handle the network configuration in order to improve network performance and monitoring. Since SDN contains the logically centralised controller which controls the entire network, the attacker mainly focuses on causing vulnerability towards the controller. Hence there is a need of powerful tool called intrusion detection system (IDS) to detect and prevent the network from various intrusions. Therefore, incorporation of IDS into SDN architecture is essential one. Nowadays, machine learning (ML) approaches can provide promising solution for the prediction of attacks with more accuracy and with low error rate. In this paper, we surveyed about some machine learning techniques such as naive Bayes, decision tree, random forest, multilayer perceptron algorithms for IDS and compare their performance in terms of attack prediction accuracy and error rate. Additionally, we also discussed about the background of SDN, security issues in SDN, overview of IDS types and various machine learning approaches with the knowledge of datasets.