Machine-Learning Techniques for Detecting Attacks in SDN

With the advent of Software Defined Networks (SDNs), there has been a rapid advancement in the area of cloud computing. It is now scalable, cheaper, and easier to manage. However, SDNs are more prone to security vulner- abilities as compared to legacy systems. Therefore, machine- learning techniques are now deployed in the SDN infrastructure for the detection of malicious traffic. In this paper, we provide a systematic benchmarking analysis of the existing machine- learning techniques for the detection of malicious traffic in SDNs. We identify the limitations in these classical machine- learning based methods, and lay the foundation for a more robust framework. Our experiments are performed on a publicly available dataset of Intrusion Detection Systems (IDSs).