| Title: |
An RF Fingerprinting Blind Identification Method Based on Deep Clustering for IoMT Security |
| Authors: |
Di Lin; Yansu Pang; Shenyuan Chen; Jun Huang; Haoqi Xian |
| Source: |
Electronics ; Volume 14 ; Issue 8 ; Pages: 1504 |
| Publisher Information: |
Multidisciplinary Digital Publishing Institute |
| Publication Year: |
2025 |
| Collection: |
MDPI Open Access Publishing |
| Subject Terms: |
RF fingerprint; deep clustering; blind recognition; IoMT |
| Description: |
To tackle the issue of unknown spoofing attacks in the Internet of Medical Things (IoMT), we put forward an iterative deep clustering model for blind RF fingerprint recognition. This model seamlessly combines a representation learning module and a clustering module, facilitating end—to—end training and optimization. Its parameters are updated according to an innovative loss function. Moreover, this model incorporates a noise—canceling self—encoder module to reduce noise and extract the noise—independent intrinsic fingerprints of devices. In comparison with other algorithms, the proposed model remarkably improves the blind recognition performance for the identification of unknown devices in the IoMT. |
| Document Type: |
text |
| File Description: |
application/pdf |
| Language: |
English |
| Relation: |
Networks; https://dx.doi.org/10.3390/electronics14081504 |
| DOI: |
10.3390/electronics14081504 |
| Availability: |
https://doi.org/10.3390/electronics14081504 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| Accession Number: |
edsbas.203EE3E6 |
| Database: |
BASE |