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, [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 ; Miettinen, Markus ; Nguyen, Thien Duc ; Sadeghi, Ahmad-Reza ; 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 |
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