IoT device identification via network-flow based fingerprinting and learning

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

4 Citations (Scopus)

Abstract

Nowadays, increasing number of intelligent devices and smart sensors are connected by Internet of Things (IoT) techniques, and have helped people to manage and improve their lives. However, security issues are emerging in IoT, among which things identification is one of the challenges in that various solutions of different vendors, standards, protocols and communities groups coexist. In this paper, we address the challenge of IoT device identification by analyzing a sequence of packets from its high-level network traffic, i.e., network-flow data and extract unique flow-based features to create a fingerprint for each device. We adopt supervised machine learning techniques for the identification task. The proposed approach can automatically identify white-listed device types and individual device instances connected to a network. Moreover, we propose a security system model design that enables enforcement of rules for constraining the IoT device communications as per their given privileges. Unknown or suspicious devices with abnormal behaviour can be identified, and their communication is restricted for further monitoring. We show that the presented approach is effective in identifying white-listed device types with average accuracy up to 90.3% which is a 9.3% increase compared with the state-of-the-art technique. Individual device instances having the same model and vendor as well as unknown devices are correctly identified with minimal performance overhead.

Original languageEnglish
Title of host publicationProceedings - 2019 18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019
Place of PublicationLos Alamitos, CA
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages103-111
Number of pages9
ISBN (Electronic)9781728127767
DOIs
Publication statusPublished - 1 Aug 2019
Event18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019 - Rotorua, New Zealand
Duration: 5 Aug 20198 Aug 2019

Publication series

NameIEEE Trustcom BigDataSE ISPA
PublisherIEEE COMPUTER SOC
ISSN (Print)2324-9013

Conference

Conference18th IEEE International Conference on Trust, Security and Privacy in Computing and Communications/13th IEEE International Conference on Big Data Science and Engineering, TrustCom/BigDataSE 2019
CountryNew Zealand
CityRotorua
Period5/08/198/08/19

Keywords

  • Automatic IoT Authentication
  • Fingerprinting
  • IoT Identification
  • IoT Security
  • Machine Learning

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