Identification of user application by an external eavesdropper using machine learning analysis on network traffic

Sina Fathi-Kazerooni, Yagiz Kaymak, Roberto Rojas-Cessa

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

3 Scopus citations

Abstract

An eavesdropper may infer the computer applications a person uses by collecting and analyzing the network traffic they generate. Such inference may be performed despite applying encryption on the generated packets. In this paper, we investigate the extent of the ability of several machine learning algorithms to perform this privacy breach on the network traffic generated by a user. We measure their accuracy in identifying different applications by analyzing several statistical properties of the generated traffic rather than looking into the encrypted content. We compare the performance of these algorithms and select the one with higher precision; random forest. We also evaluate the application of packet padding to modify the packet length to avoid identification by machine learning algorithms. We test the effect of packet padding on the identification ability of the various machine-learning algorithms. We investigate the performance of the random forest algorithm in detail when applied to intact and padded traffic. We show that padding may decrease the efficacy of a machine-learning algorithm when used for application classification.

Original languageEnglish (US)
Title of host publication2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728123738
DOIs
StatePublished - May 2019
Event2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Shanghai, China
Duration: May 20 2019May 24 2019

Publication series

Name2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019 - Proceedings

Conference

Conference2019 IEEE International Conference on Communications Workshops, ICC Workshops 2019
Country/TerritoryChina
CityShanghai
Period5/20/195/24/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Hardware and Architecture
  • Aerospace Engineering

Keywords

  • Internet Traffic Classification
  • Machine Learning
  • Multi-layer perceptron
  • Online activity tracking
  • Random Forest
  • Support-vector machines

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