ProfilIoT: A machine learning approach for IoT device identification based on network traffic analysis

Yair Meidan, Michael Bohadana, Asaf Shabtai, Juan David Guarnizo, Martín Ochoa, Nils Ole Tippenhauer, Yuval Elovici

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

212 Scopus citations

Abstract

In this work we apply machine learning algorithms on network trafic data for accurate identification of IoT devices connected to a network. To train and evaluate the classifier, we collected and labeled network trafic data from nine distinct IoT devices, and PCs and smartphones. Using supervised learning, we trained a multi-stage meta classifier; in the first stage, the classifier can distinguish between trafic generated by IoT and non-IoT devices. In the second stage, each IoT device is associated a specific IoT device class. The overall IoT classification accuracy of our model is 99.281%.

Original languageEnglish
Title of host publication32nd Annual ACM Symposium on Applied Computing, SAC 2017
PublisherAssociation for Computing Machinery
Pages506-509
Number of pages4
ISBN (Electronic)9781450344869
DOIs
StatePublished - 3 Apr 2017
Event32nd Annual ACM Symposium on Applied Computing, SAC 2017 - Marrakesh, Morocco
Duration: 4 Apr 20176 Apr 2017

Publication series

NameProceedings of the ACM Symposium on Applied Computing
VolumePart F128005

Conference

Conference32nd Annual ACM Symposium on Applied Computing, SAC 2017
Country/TerritoryMorocco
CityMarrakesh
Period4/04/176/04/17

Keywords

  • Cyber security
  • Device identification
  • Internet of Things (IoT)
  • Machine learning
  • Network trafic analysis

ASJC Scopus subject areas

  • Software

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