Description

Network traffic classification (NTC) has attracted considerable attention in recent years. The importance of traffic classification stems from the fact that data traffic in modern networks is extremely complex and ever-evolving in different aspects. The inherent security requirements of Internet-based applications also highlights further the role of traffic classification. Therefore, developing Machine Learning (ML) models, which can successfully identify network applications, is one of the most important tasks. However, among the ML models applied to network traffic classification so far, no model outperforms all the others. To solve these issues, our proposed approach based Deep Learning (DL) Algorithm. PCA algorithm is used feature extraction. Ensemble learning combines several individual models to obtain better generalization performance. This ensemble consists of two levels called base classifiers and meta-classifiers. Deep ensemble learning models as well as the ensemble learning such that the final model has better generalization performance. The outputs of the models are then combined to generate the final prediction.