Fast and Accurate Multi-Task Learning for Encrypted Network Traffic Classification

The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduce...

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Bibliographic Details
Published in:Applied sciences 2024-04, Vol.14 (7), p.3073
Main Authors: Park, Jee-Tae, Shin, Chang-Yui, Baek, Ui-Jun, Kim, Myung-Sup
Format: Article
Language:English
Subjects:
Analysis
BERT
Classification
Computational linguistics
Datasets
Deep learning
Efficiency
encrypted traffic classification
Language processing
Machine learning
Methods
multi-task classification
Natural language interfaces
transformer
Virtual private networks
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Summary:The classification of encrypted traffic plays a crucial role in network management and security. As encrypted network traffic becomes increasingly complicated and challenging to analyze, there is a growing need for more efficient and comprehensive analytical approaches. Our proposed method introduces a novel approach to network traffic classification, utilizing multi-task learning to simultaneously train multiple tasks within a single model. To validate the proposed method, we conducted experiments using the ISCX 2016 VPN/Non-VPN dataset, consisting of three tasks. The proposed method outperformed the majority of existing methods in classification with 99.29%, 97.38%, and 96.89% accuracy in three tasks (i.e., encapsulation, category, and application classification, respectively). The efficiency of the proposed method also demonstrated outstanding performance when compared to methods excluding lightweight models. The proposed approach demonstrates accurate and efficient multi-task classification on encrypted traffic.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14073073