On Traffic Classification in Enterprise Wireless Networks

Abstract

Enterprises today are quickly adopting intelligent, adaptive, and flexible wireless communication technologies in order to become compliant with Industry 4.0. One of the technological challenges related to this is to provide Quality of Services (QoS)-enabled network connectivity to the applications. Diverse QoS demands from the applications intimidate the underlying wireless networks to be agile and adaptive. Since the applications are diverse in nature, there must be a mechanism to learn the application types in near real-time so that the network can be provisioned accordingly. In this paper, we propose a Machine Learning (ML) based method to classify the application traffic. Our method is different from the existing port based and Deep Packet Inspection (DPI) based methods and uses statistical features of the network traffic related to the applications. We validate the performance of the proposed model in a lab based SDNized WiFi set-up. SDNization ensures that the proposed model can be deployed in practice.