Network Traffic Classification Using Supervised Machine Learning Algorithms in Systems with NFV Architecture

Abstract

AbstractNetwork functions virtualization architecture concept is gaining more popularity and it is used in different systems. Together with the cloudification within public, private and mixed clouds it is becoming a base for the future development of the digital world. The concepts of containers, virtual network functions, application functions are cohered within the clouds and guided with the NFV systems. Another aspect which is developing rapidly are the access technologies, especially the 5G, which is the all expected enabler of the IoT. Within such circumstances, most of the network traffic is expected to flow in the east–west direction, never leaving the cloud. Our work if focused on preparation of experimental environment that will simulate such traffic. We are analysing the traffic by making classification of the network data flows, using a selected set of six supervised machine learning (ML) algorithms. The goal of our research is to find the algorithm with the best performance within the prepared environment. We define the performance as a combination of the ML algorithm's classification precision, and the time consumption of the algorithm, which bears a great significance, especially from a point of 5G, where any packet delay introduced within the system may compromise the 5G specification calls for latency. From the research we conclude that out of the 6 explored ML algorithms, the Decision Tree algorithms is the most suitable classifier that fits within the needed precision across all classes, but also within the time consumption needs. Our approach also considers the regulatory point of view for automated data analysis within systems, and we deal only with statistical features of the network flows, while the payload data, the source and destination information, as well as the network port, are excluded as attributes used for classification, especially as we deal with VoIP and encrypted VoIP data that is used in 5G.