Network Traffic Analysis and Control by Application of Machine Learning

Abstract

The purpose of this paper is to analyses how networks work, how data is transmitted, what information we get from each router during data transmission, getting to know the basics of machine learning and how to create models that will learn how networks work. By applying machine learning methods, results are obtained that show us the shape of a network. With different methods we can get information about how we can plan the network, in terms of expanding the network if the capacity of the links is almost full or when one of the links has predispositions to go from an active state to an inactive one. The results show satisfactory outcomes through the use of three different machine learning models that were capable of accurately detecting the functionality of a port, calculating its utilization and learning when the utilization hits a threshold of above 75%.