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

Marchal, Samuel ; Miettinen, Markus ; Nguyen, Thien Duc ; Sadeghi, Ahmad-Reza ; Asokan, N. (2019)
AuDI: Towards autonomous IoT device-type identification using periodic communications.
In: IEEE Journal on Selected Areas in Communications
Article, Primary publication

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Item Type: Article
Type of entry: Primary publication
Title: AuDI: Towards autonomous IoT device-type identification using periodic communications
Language: English
Date: June 2019
Place of Publication: Darmstadt
Publisher: IEEE
Journal or Publication Title: IEEE Journal on Selected Areas in Communications
Abstract:

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).

URN: urn:nbn:de:tuda-tuprints-85117
Additional Information:

Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications

Classification DDC: 000 Generalities, computers, information > 004 Computer science
Divisions: 20 Department of Computer Science > System Security Lab
Date Deposited: 25 Mar 2019 08:40
Last Modified: 26 Aug 2022 05:30
URI: https://tuprints.ulb.tu-darmstadt.de/id/eprint/8511
PPN: 498643034
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