AuDI: Towards autonomous IoT device-type identification using periodic communications

Samuel Marchal, Markus Miettinen, Thien Duc Nguyen, Ahmad-Reza Sadeghi, N. Asokan

Research output: Contribution to journalArticleScientificpeer-review

153 Citations (Scopus)
841 Downloads (Pure)


IoT devices are being widely deployed. But the huge variance among them in the level of security and requirements for network resources makes it unfeasible to manage IoT networks using a common generic policy. One solution to this challenge is to define policies for classes of devices based on device type. In this paper, we present AuDI, a system for quickly and effectively identifying the type of a device in an IoT network by analyzing their network communications. AuDI models the periodic communication traffic of IoT devices using an unsupervised learning method to perform identification. In contrast to prior work, AuDI operates autonomously after initial setup, learning, without human intervention nor labeled data, to identify previously unseen device types. AuDI can identify the type of a device in any mode of operation or stage of lifecycle of the device. Via systematic experiments using 33 off-the-shelf IoT devices, we show that AuDI is effective (98.2% accuracy).

Original languageEnglish
Article number8664655
Pages (from-to)1402-1412
Number of pages11
JournalIEEE Journal on Selected Areas in Communications
Issue number6
Publication statusPublished - 1 Jun 2019
MoE publication typeA1 Journal article-refereed


  • Internet of Things
  • device-type identification
  • autonomous IoT device identification
  • self-learning


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