Similarity-Based Clustering For IoT Device Classification

Guillaume Dupont, Cristoffer Leite, Daniel Ricardo dos Santos, Elisa Costante, Jerry den Hartog, Sandro Etalle

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


Classifying devices connected to an enterprise network is a fundamental security control that is nevertheless challenging due to the limitations of fingerprint-based classification and black-box machine learning. In this paper, we address such limitations by proposing a similarity-based clustering method. We evaluate our solution and compare it to a state-of-the-art fingerprint-based classification engine using data from 20,000 devices. The results show that we can successfully classify around half of the unclassified devices with a high accuracy. We also validate our approach with domain experts to demonstrate its usability in producing new fingerprinting rules.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2021
Number of pages7
ISBN (Electronic)9781665431569
ISBN (Print)978-1-6654-3157-6
Publication statusPublished - 25 Aug 2021
Event2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS) - Barcelona, Spain
Duration: 23 Aug 202125 Aug 2021


Conference2021 IEEE International Conference on Omni-Layer Intelligent Systems (COINS)


  • Performance evaluation
  • Semantics
  • Process control
  • Machine learning
  • Manuals
  • Fingerprint recognition
  • Reliability engineering
  • Classification
  • Clustering
  • Internet of Things


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