TU Darmstadt / ULB / TUbiblio

AUDI: Towards Autonomous IoT Device-Type Identification

Marchal, Samuel and Miettinen, Markus and Nguyen, Thien Duc and Sadeghi, Ahmad-Reza and 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, [Article]

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).

Item Type: Article
Erschienen: 2019
Creators: Marchal, Samuel and Miettinen, Markus and Nguyen, Thien Duc and Sadeghi, Ahmad-Reza and Asokan, N.
Title: AUDI: Towards Autonomous IoT Device-Type Identification
Language: English
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).

Journal or Publication Title: IEEE Journal on Selected Areas in Communications (JSAC) on Artificial Intelligence and Machine Learning for Networking and Communications
Uncontrolled Keywords: ICRI-CARS; Solutions; S2; Primitives; P3
Divisions: 20 Department of Computer Science
20 Department of Computer Science > System Security Lab
DFG-Collaborative Research Centres (incl. Transregio)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres
Profile Areas
Profile Areas > Cybersecurity (CYSEC)
DFG-Collaborative Research Centres (incl. Transregio) > Collaborative Research Centres > CRC 1119: CROSSING – Cryptography-Based Security Solutions: Enabling Trust in New and Next Generation Computing Environments
Date Deposited: 19 Feb 2019 09:40
Export:

Optionen (nur für Redakteure)

View Item View Item