Tracking User Application Activity by using Machine Learning Techniques on Network Traffic

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

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

4 Scopus citations

Abstract

A network eavesdropper may invade the privacy of an online user by collecting the passing traffic and classifying the applications that generated the network traffic. This collection may be used to build fingerprints of the user's Internet usage. In this paper, we investigate the feasibility of performing such breach on encrypted network traffic generated by actual users. We adopt the random forest algorithm to classify the applications in use by users of a campus network. Our classification system identifies and quantifies different statistical features of user's network traffic to classify applications rather than looking into packet contents. In addition, application classification is performed without employing a port mapping at the transport layer. Our results show that applications can be identified with an average precision and recall of up to 99%.

Original languageEnglish (US)
Title of host publication1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages405-410
Number of pages6
ISBN (Electronic)9781538678220
DOIs
StatePublished - Mar 18 2019
Event1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019 - Okinawa, Japan
Duration: Feb 11 2019Feb 13 2019

Publication series

Name1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019

Conference

Conference1st International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2019
Country/TerritoryJapan
CityOkinawa
Period2/11/192/13/19

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Computer Science Applications
  • Artificial Intelligence

Keywords

  • Internet Traffic Classification
  • Machine Learning
  • Online Activity Tracking
  • Random Forest

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