Server-Side Distinction of Incoming Traffic Transmission Medium Using Machine Learning

Abstract

In the two decades, there have been rapid advancements in the field of communication and information technology. Today, infrastructures include high-speed broadband networks that serve users at a fixed location which is referred to as fixed broadband networks. Another type serves users on the move which is referred to as mobile broadband networks. Fixed broadband networks offer fixed speed and reliable connections. Meanwhile, mobile broadband networks offer mobility, but they are less reliable. Therefore, some of the critical operations such as system update require users to be connected to a fixed broadband network. Different type of networks results in different type of traffic pattern. This paper utilizes a machine learning model at the server-side to help servers differentiate between data transmitted over fixed networks and data transmitted over mobile networks. Supervised training was used to build the model. The proposed system was tested and it showed an accuracy of 92.24 percent. This work is novel and the first of its kind since it is the first to attempt the detection of the nature of the network used for transmission based on the pattern of the arrival of packets.