IoTDevID: A Behavior-Based Device Identification Method for the IoT

Kahraman Kostas, Mike Just, Michael Adam Lones

Research output: Contribution to journalArticlepeer-review


Device identification is one way to secure a network of IoT devices, whereby devices identified as suspicious can subsequently be isolated from a network. In this study, we present a machine learning-based method, IoTDevID, that recognizes devices through characteristics of their network packets. As a result of using a rigorous feature analysis and selection process, our study offers a generalizable and realistic approach to modelling device behavior, achieving high predictive accuracy across two public datasets. The model’s underlying feature set is shown to be more predictive than existing feature sets used for device identification, and is shown to generalize to data unseen during the feature selection process. Unlike most existing approaches to IoT device identification, IoTDevID is able to detect devices using non-IP and low-energy protocols.
Original languageEnglish
JournalIEEE Internet of Things Journal
Early online date18 Jul 2022
Publication statusE-pub ahead of print - 18 Jul 2022


  • Behavioral sciences
  • Feature extraction
  • Fingerprint recognition
  • Internet of Things
  • IoT security
  • Object recognition
  • Protocols
  • Security
  • fingerprinting
  • machine learning

ASJC Scopus subject areas

  • Signal Processing
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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