  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 vulnerabilities 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).
