On using eXtreme Gradient Boosting (XGBoost) Machine Learning algorithm for Home Network Traffic Classification
This work considers a supervised approach, namely eXtreme Gradient Boosting (XGBoost) algorithm, which has never been investigated for TC, and obtains 99.5% accuracy on a dataset containing real flows.
Abstract
Traffic classification (TC) is a fundamental task of network management and monitoring operations. Previous works relying on selected packet header fields (e.g. port numbers) or application layer protocol decoding techniques are becoming increasingly difficult and inefficient when facing encrypted traffic and peer-to-peer flows. In this paper, we address the problem of flow based TC using machine learning (ML) algorithms. Our work considers a supervised approach, namely eXtreme Gradient Boosting (XGBoost) algorithm, which has never been investigated for TC. Performance evaluation results show that we obtain 99.5% accuracy on a dataset containing real flows. Additionally, compared to other ML algorithms, XGBoost is the most accurate one.