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AUDI: Towards Autonomous IoT Device-Type Identification

Marchal, Samuel ; Miettinen, Markus ; Nguyen, Thien Duc ; Sadeghi, Ahmad-Reza ; Asokan, N. (2019)
AUDI: Towards Autonomous IoT Device-Type Identification.
In: IEEE Journal on Selected Areas in Communications (JSAC) on Artificial Intelligence and Machine Learning for Networking and Communications
Artikel, Bibliographie

Kurzbeschreibung (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 infeasible 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).

Typ des Eintrags: Artikel
Erschienen: 2019
Autor(en): Marchal, Samuel ; Miettinen, Markus ; Nguyen, Thien Duc ; Sadeghi, Ahmad-Reza ; Asokan, N.
Art des Eintrags: Bibliographie
Titel: AUDI: Towards Autonomous IoT Device-Type Identification
Sprache: Englisch
Publikationsjahr: 19 Februar 2019
Titel der Zeitschrift, Zeitung oder Schriftenreihe: IEEE Journal on Selected Areas in Communications (JSAC) on Artificial Intelligence and Machine Learning for Networking and Communications
Kurzbeschreibung (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 infeasible 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).

Freie Schlagworte: ICRI-CARS; Solutions; S2; Primitives; P3
Fachbereich(e)/-gebiet(e): 20 Fachbereich Informatik
20 Fachbereich Informatik > Systemsicherheit
DFG-Sonderforschungsbereiche (inkl. Transregio)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche
Profilbereiche
Profilbereiche > Cybersicherheit (CYSEC)
DFG-Sonderforschungsbereiche (inkl. Transregio) > Sonderforschungsbereiche > SFB 1119: CROSSING – Kryptographiebasierte Sicherheitslösungen als Grundlage für Vertrauen in heutigen und zukünftigen IT-Systemen
Hinterlegungsdatum: 19 Feb 2019 09:40
Letzte Änderung: 16 Mai 2019 12:53
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