Enhancing Encrypted Traffic Classification with Deep Adaptation Networks

Cuong Dao, Van Tong, Nam Thang Hoang, Hai Anh Tran, Truong X. Tran

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

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.

Original languageEnglish (US)
Title of host publicationProceedings of the 48th IEEE Conference on Local Computer Networks , LCN 2023
EditorsEyuphan Bulut, Florian Tschorsch, Kanchana Thilakarathna
PublisherIEEE Computer Society
ISBN (Electronic)9798350300734
DOIs
StatePublished - 2023
Event48th IEEE Conference on Local Computer Networks , LCN 2023 - Daytona Beach, United States
Duration: Oct 2 2023Oct 5 2023

Publication series

NameProceedings - Conference on Local Computer Networks, LCN

Conference

Conference48th IEEE Conference on Local Computer Networks , LCN 2023
Country/TerritoryUnited States
CityDaytona Beach
Period10/2/2310/5/23

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture

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