Traffic Classification of SDN Network using Machine Learning Algorithms

Traffic classification is becoming an important research area especially with the day-to-day machine learning software defined networking advancements. It is utilized for networking executives, administration estimation, network plan, security observing, and publishing. The proposed model used two machine learning models logistic regression and kmeans are used to classify ping, telnet, dns, and voice flows(VoIP) with the utilization of an Internet-based motor (DITG) that utilizes two techniques for Artificial Intelligence (AI). The host on these organizations is associated with the open vswitch (Ovs) over the two organizations. The Ovs are joined by a reviewer named Ryu (signifying "streaming" in Japanese. Ryu is designated "ree-yooh") and gathers street figures between the beneficiaries. These figures are examined by Python's robotized archive arranging framework and learned street signs are carried out. The proposed work will classify the type of network traffic flowing between two hosts.