The Usage Analysis of Machine Learning Methods for Intrusion Detection in Software-Defined Networks

Abstract

This chapter focuses on the process of the machine learning with considering the architecture of software-defined networks (SDNs) and their security mechanisms. In general, machine learning has been studied widely in traditional network problems, but recently there have been a limited number of studies in the literature that connect SDN security and machine learning approaches. The main reason of this situation is that the structure of SDN has emerged newly and become different from the traditional networks. These structural variances are also summarized and compared in this chapter. After the main properties of the network architectures, several intrusion detection studies on SDN are introduced and analyzed according to their advantages and disadvantages. Upon this schedule, this chapter also aims to be the first organized guide that presents the referenced studies on the SDN security and artificial intelligence together.

Traffic Optimization with Software-Defined Network Controller on a New User Interface

“…SDN presents advantages of hardware independence, simplification of network control and applications with software, flexibility, dynamic network configuration, and system scalability [Akbaş et al 2016], [Özbek et al 2020]. One of the most popular SDN architectures in the world is B4 wide area network of Google [Yiltas-Kaplan 2019].…”

“…There are several software examples, such as Floodlight, working as the controller on the control plane. The top layer of SDN, namely the application layer, incorporates all the SDN applications and has connections to the controller via the northbound interface to provide various network requirements [Akçay and Yiltas-Kaplan 2019], [Open Networking Foundation 2014], [Yiltas-Kaplan 2019].…”