Traffic classification with on-line ensemble method

Abstract

Traffic classification helps network managers to control services and activities done by users. Traditionally, Machine Learning (ML) is a tool to help managers to detect applications most used, and offer different types of services to their clients. Most of ML algorithms are designed to deal with limited amount of data, and in network context this is a problem, because of large data volume, speed and diversity. More recent work try to solve this issue by using ML algorithms developed to work with data streams, but they tend to implement only Very Fast Decision Trees (VFDT). This work goes in a different direction by proposing to use Ensemble Learners (EL), which, theoretically, offer more capability to deal with non-linear problems. The paper proposes to use a new EL called OzaBoost Dynamic (OzaDyn), and compares its performance with other ensemble methods designed to deal with data streams. Results indicate that the accuracy performance of OzaDyn is equal to other ensemble methods, while it helps reduce the memory consumption and time to evaluate the models.