Multi-Level IoT Device Identification
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
Author(s)
Related Research Unit(s)
Detail(s)
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2021 IEEE 27th International Conference on Parallel and Distributed Systems |
| Subtitle of host publication | ICPADS 2021 |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. |
| Pages | 538-547 |
| ISBN (electronic) | 9781665408783 |
| ISBN (print) | 978-1-6654-0879-0 |
| Publication status | Published - Dec 2021 |
Publication series
| Name | Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS |
|---|---|
| ISSN (Print) | 1521-9097 |
| ISSN (electronic) | 2690-5965 |
Conference
| Title | 27th IEEE International Conference on Parallel and Distributed Systems (ICPADS 2021) |
|---|---|
| Location | Jiuhua International Convention and Exhibition Center Hotel |
| Place | China |
| City | Beijing |
| Period | 14 - 16 December 2021 |
Link(s)
Abstract
The rapid development of the Internet of Things (IoT) has brought challenges to IoT platforms for high-efficiency deployments and low-budget management. Identifying IoT devices is the prerequisite for monitoring, protecting, and managing them. Considering different providers and IoT device renovation, centralized device identification solutions require large amounts of training data and frequent model updates. Traditional solutions based on machine learning cannot preserve identification precision for the long term at a low cost in reality. In this paper, we propose a multi-level IoT device identification framework, alleviating the problem of novel class detection and large-scale updating of IoT models in IoT device identification. The proposed framework improves the usability of device identification technology in the real world. We also designed an IoT device identification method, achieving an average identification accuracy of 93.37 %. With this proposed multi-level IoT device identification framework, IoT device identification can achieve a high precision over a long time.
Research Area(s)
- Device Fingerprinting, Devices Identification, IoT Security
Bibliographic Note
Full text of this publication does not contain sufficient affiliation information. With consent from the author(s) concerned, the Research Unit(s) information for this record is based on the existing academic department affiliation of the author(s).
Citation Format(s)
Multi-Level IoT Device Identification. / Jiao, Ruohong; Liu, Zhe; Liu, Liang et al.
Proceedings - 2021 IEEE 27th International Conference on Parallel and Distributed Systems: ICPADS 2021. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 538-547 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS).
Proceedings - 2021 IEEE 27th International Conference on Parallel and Distributed Systems: ICPADS 2021. Institute of Electrical and Electronics Engineers, Inc., 2021. p. 538-547 (Proceedings of the International Conference on Parallel and Distributed Systems - ICPADS).
Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review
