Building Machine Learning Based Firewall on Spanning Tree Protocol over Software Defined Networking

摘要

Software-Defined Networking (SDN) is the most expected inventions for the network administrators, cloud service providers and businessmen. For its global acceptance, has to undergo security issues and get cover using a proper solution. This study implements the firewall on the Spanning Tree Protocol (STP) over SDN using Mininet emulator. The broadcast streams are controlled by the Firewall on STP based SDN without dedicated hardware support. The result focuses the Firewall functionalities over STP based SDN as well as frees the available bandwidth for traffic. Additionally, Machine Learning (ML) techniques have been applied to build up the infrastructure to get the best outcome from a model with top-notch performance. Among different machine learning models using the same NSL-KDD dataset, Random Forest Classifier algorithm stands at the top with highest accuracy in differentiating between regular communication and intrusion on the network.