Traffic Classification of Home Network Devices using Supervised Learning

Abstract

Network traffic classification is a relevant tool for computer network management. In the last decade, researchers have been adopting machine learning algorithms to identify different types of traffic in a network. Traffic classification can be used to identify threats and improve the quality of service of networks. Literature in this area usually focuses on using network flows to identify the traffic of specific devices, for example, IoT devices. This paper proposes a network traffic classification model to identify IoT smart home devices and personal computers (PCs). The idea is to evaluate the performance of decision models trained with different devices to identify IoT and non-IoT network traffic. We created two scenarios to mimic the behavior of a home network. In the first scenario, we evaluate how training a model with only PC devices influences the identification of IoT and non-IoT traffic. The second one attempts to assess how well the network traffic of a brand new type of IoT device could be identified using supervised learning. Our results show that the supervised models were able to identify the network traffic; however, their performance varies across the algorithms.