Decision tree-based blending method using deep-learning for network management

Network traffic classification is a key component for network management, Quality-of-Service management, as well as for network security. Therefore, developing machine learning (ML) methods, which can successfully distinguish network applications from each other, is one of the most important tasks. However, among the classification methods applied to network traffic classification so far, there is no one method that outperforms all the others. They all have advantages and inconveniences depending on the application domain. Therefore,this paper proposes an intelligent traffic management model by using deep learning (DL) that incorporates multiple Decision Tree-based models. Our model deploys a blending ensemble learning method to combine tree-based classifiers in order to maximize generalization accuracy. Using two datasets, we show that our proposed ensemble model is efficient for network traffic classification. Furthermore, the proposed approach is also compared against other representative machine-learning and deep-learning models and the results demonstrate that our approach provides better performance compared to others.