Machine Learning is a powerful tool that provides reasonable capabilities for identifying security breaches. The implementation of Machine Learning in SDN has been one of the notable aspects of the platform. ML-based SDN security models rituals a huge influence on switching/routing on the entire SDN. Recently, ML-based SDN security applications have looked great in ML-specific vulnerabilities accompanied by common attack methods to make a case for shielded development processes. The ML-based SDN security applications are synthetic and not realistic enough. It also faces some security issues:

 •  Hard to find organic training data
 •  Measuring and Minimizing the cost of errors
 •  Enormous variability in input data
 •  A semantic gap between initial work and practical real-world deployments

The ML-based SDN methods have used numerous techniques to detect anomalies in systems that support,

 •  Support vector machine (SVM)
 •  Naive Bayes
 •  Decision Tree
 •  Logistic Regression

The SDN-based ML controls become the most attractive venues for attacks for Denial of Service (DoS) and Distributed Denial of Service (DDoS). Due to the success and benefits of ML in fingerprint security vulnerabilities, it can be evaluated in a practical setup where the SDN controller is exposed to DDOS attacks for an important conclusion of ML-based security of future network communications.

The Machine Learning SDN can be used to process traffic flows based on externally observable properties such as,

 •  Flow duration
 •  Packet size
 •  Flow size in bytes
 •  Interpacket arrival time
 •  Number and packets in a flow

Therefore, evaluating ML techniques allows users to learn about models and patterns from the given data. Four categories of various ML techniques are roughly presented such as,

 •  Supervised
 •  Unsupervised
 •  Semi-supervised
 •  Reinforcement Learning

From the security perspective, three different outlines can be plotted, namely,

 •  Anomaly detection
 •  Botnet detection
 •  Honeypot traffic rerouting