.png){kind=icon}
Return
Deep Ensemble Learning for Application Traffic Classification Using Differential Model Selection Technique
.png){kind=icon}
OA
AI
DOI：10.3390/s25092853.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
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.
Keywords:
deep ensemble
model selection technique
application traffic classification
end-to-end ensemble learning
network management
Journal
IF:
3.5
Papers:
7.2W
Citations:
20.9W
Organization
Cited Papers
ShareSaveShareSaveA Survey of Ensemble Learning: Concepts, Algorithms, Applications, and Prospects.png)
IEEE ACCESSOAAI
IEEE ACCESSIF3.6
OAAIShareSave
ShareSaveShareSave
View more