Marchal, Samuel ; Miettinen, Markus ; Nguyen, Thien Duc ; Sadeghi, Ahmad-Reza ; Asokan, N. (2019)
AuDI: Towards autonomous IoT device-type identification using periodic communications.
Report, Erstveröffentlichung, Preprint
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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 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).
| Typ des Eintrags: | Report |
|---|---|
| Erschienen: | 2019 |
| Autor(en): | Marchal, Samuel ; Miettinen, Markus ; Nguyen, Thien Duc ; Sadeghi, Ahmad-Reza ; Asokan, N. |
| Art des Eintrags: | Erstveröffentlichung |
| Titel: | AuDI: Towards autonomous IoT device-type identification using periodic communications |
| Sprache: | Englisch |
| Publikationsjahr: | Juni 2019 |
| Ort: | Darmstadt |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | IEEE Journal on Selected Areas in Communications |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/8511 |
| 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 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). |
| Status: | Preprint |
| URN: | urn:nbn:de:tuda-tuprints-85117 |
| Zusätzliche Informationen: | Ersch. in: IEEE Journal on Selected Areas in Communications 2019; Special Issue on Artificial Intelligence and Machine Learning for Networking and Communications |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Systemsicherheit |
| Hinterlegungsdatum: | 31 Mär 2019 19:55 |
| Letzte Änderung: | 16 Okt 2024 09:36 |
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| Suche nach Titel in: | TUfind oder in Google |
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