ETCNet: Encrypted Traffic Classification using Siamese Convolutional Networks

Lu Xu, Daihui Dou, H. Jonathan Chao

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

Abstract

As more and more Internet traffic is encrypted, classifying their flows for the usage in application-aware networking (AAN) and application-network integration (ANI) becomes increasingly important and challenging. Traditional deep packet inspection approaches are no longer capable of identifying the encrypted packet streams, and hence new traffic classification methods based on machine learning have recently been explored by several researchers. One major challenge of using machine learning to classify encrypted traffic is lacking real datasets. Collecting Internet traffic may leak users' sensitive information, which prohibits the network community from sharing the datasets they collected. In this poster, we propose ETCNet which is a model based on Siamese convolutional network to solve this issue. Our evaluation for the ETCNet shows that it can achieve high accuracy by only using 40 flows of each application to train it.

Original languageEnglish (US)
Title of host publicationNAI 2020 - Proceedings of the 2020 Workshop on Network Application Integration/CoDesign
PublisherAssociation for Computing Machinery
Pages51-53
Number of pages3
ISBN (Electronic)9781450380447
DOIs
StatePublished - Aug 14 2020
Event1st Workshop on Network Application Integration/CoDesign, NAI 2020, colocated with ACM SIGCOMM 2020 - Virtual, Online, United States
Duration: Aug 14 2020 → …

Publication series

NameNAI 2020 - Proceedings of the 2020 Workshop on Network Application Integration/CoDesign

Conference

Conference1st Workshop on Network Application Integration/CoDesign, NAI 2020, colocated with ACM SIGCOMM 2020
CountryUnited States
CityVirtual, Online
Period8/14/20 → …

Keywords

  • encrypted traffic classification
  • machine learning
  • siamese convolutional network

ASJC Scopus subject areas

  • Software
  • Hardware and Architecture

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