Deep Ensemble Learning for Application Traffic Classification Using Differential Model Selection Technique

As the Internet evolves, application traffic is becoming increasingly diverse and complex, leading network administrators to demand more accurate application traffic classification. Various deep learning-based application traffic classification methods have clearly achieved significant success, demonstrating superior classification performance compared to traditional heuristic classification approaches. However, achieving accuracy while maintaining time-efficiency and high generalization performance remains a challenge. We propose an end-to-end learning method that incorporates a model-selection-based ensemble mechanism to improve the performance-inference time trade-off of application traffic classifiers. Evaluated on two public datasets and one private dataset, our proposed method improves classification accuracy across all datasets while ensuring reasonable inference times compared to nine classification methods.