TY - GEN
T1 - Enhancing Encrypted Traffic Classification with Deep Adaptation Networks
AU - Dao, Cuong
AU - Tong, Van
AU - Hoang, Nam Thang
AU - Tran, Hai Anh
AU - Tran, Truong X.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - Network traffic management is crucial in Computer Networks and the Internet of Things. Indeed, classifying network traffic is the foundation for enhancing the quality of management mechanisms. However, traditional traffic classification methods, such as port-based, deep packet inspection, and statistic-based, are limited in identifying new encrypted traffic characteristics. Deep Learning-based classification approaches that consider packet-based features have been explored to address this challenge. Along with other deep learning methods, Transfer Learning, where a new model can inherit knowledge previously learned by a base model, is commonly used to increase classification performance in low data resources. Unfortunately, feature transferability may decline in transfer learning. This paper proposes an encrypted traffic classification mechanism that leverages the Deep Adaptation Network architecture with Mean Embedding Test to overcome this limitation. Our experimental results show that the proposed mechanism surpasses existing benchmarks’ accuracy and can classify encrypted traffic in real-time.
AB - Network traffic management is crucial in Computer Networks and the Internet of Things. Indeed, classifying network traffic is the foundation for enhancing the quality of management mechanisms. However, traditional traffic classification methods, such as port-based, deep packet inspection, and statistic-based, are limited in identifying new encrypted traffic characteristics. Deep Learning-based classification approaches that consider packet-based features have been explored to address this challenge. Along with other deep learning methods, Transfer Learning, where a new model can inherit knowledge previously learned by a base model, is commonly used to increase classification performance in low data resources. Unfortunately, feature transferability may decline in transfer learning. This paper proposes an encrypted traffic classification mechanism that leverages the Deep Adaptation Network architecture with Mean Embedding Test to overcome this limitation. Our experimental results show that the proposed mechanism surpasses existing benchmarks’ accuracy and can classify encrypted traffic in real-time.
UR - http://www.scopus.com/inward/record.url?scp=85182949311&partnerID=8YFLogxK
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U2 - 10.1109/LCN58197.2023.10223333
DO - 10.1109/LCN58197.2023.10223333
M3 - Conference contribution
AN - SCOPUS:85182949311
T3 - Proceedings - Conference on Local Computer Networks, LCN
BT - Proceedings of the 48th IEEE Conference on Local Computer Networks , LCN 2023
A2 - Bulut, Eyuphan
A2 - Tschorsch, Florian
A2 - Thilakarathna, Kanchana
PB - IEEE Computer Society
T2 - 48th IEEE Conference on Local Computer Networks , LCN 2023
Y2 - 2 October 2023 through 5 October 2023
ER -