Efficiency of Supervised Machine Learning Algorithms in Regular and Encrypted VoIP Classification within NFV Environment

Abstract

Cloudification of all computing environments is an undergoing process. The process has overpassed the classical Virtual Machines (VM) and Software-Defined Networking (SDN) approach and has moved towards dockerizing, microservices, app functions, network functions etc. 5G penetration is another trend, and it is built on such platforms. In this environment we are investigating the efficiency of supervised machine learning algorithms for classification of regular and encrypted Voice over IP (VoIP) traffic that 5G relies on, within a virtualized Network Functions Virtualization (NFV) environment and an east-west based network traffic. We are using statistical methods for classification of network packets without the need of inspecting the payload data and without the source, destination and port information of the packets. The efficiency is analyzed from a point of precision of the classification, but also from a point of time consumption, as adding delay to the original traffic may cause a problem, especially within 5G environments where packet delay is crucial.