Machine Learning Aided Traffic Tolerance To Improve Resilience For Software Defined Networks

Software Defined Networks (SDNs) have gained prominence recently due to their flexible management and superior configuration functionality of the underlying network. SDNs, with OpenFlow as their primary implementation, allow for the use of a centralised controller to drive the decision making for all the supported devices in the network and manage traffic through routing table changes for incoming flows. In conventional networks, machine learning has been shown to detect malicious intrusion, and classify attacks such as DoS, user to root, and probe attacks. In this work, we extend the use of machine learning to improve traffic tolerance for SDNs. To achieve this, we extend the functionality of the controller to include a resilience framework, ReSDN, that incorporates machine learning to be able to distinguish DoS attacks, focussing on a neptune attack for our experiments. Our model is trained using the MIT KDD 1999 dataset. The system is developed as a module on top of the POX controller platform and evaluated using the Mininet simulator.