Research Repository

DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings

Wu, Weiwei and Hu, Su and Lin, Di and Liu, Zilong (2022) 'DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings.' IEEE Internet of Things Journal, 9 (5). pp. 3838-3849. ISSN 2327-4662

DSLN_Securing_Internet_of_Things_Through_RF_Fingerprint_Recognition_in_Low-SNR_Settings.pdf - Accepted Version

Download (1MB) | Preview


The explosive growth of Internet of things (IoT) has mandated the security of data access. Although authentication methods can enhance network security, their vulnerability to malicious attacks may be a barrier for the wide deployments in IoT scenarios. To address the security issue, we advocate the use of physical layer security through radio-frequency (RF) fingerprint recognition. Observing that most RF fingerprint recognition methods show a degradation of performance under low signal-to-noise ratio (SNR) environments, we present a dynamic shrinkage learning network (DSLN) to enhance security for IoT applications, particularly in the setting of low SNR. We design a novel dynamic shrinkage threshold for improving the accuracy of recognition under low-SNR environments. Additionally, we design an identity shortcut for reducing the running time of RF fingerprint recognition. In comparison with convolutional neural network (CNN), recurrent neural network (RNN) and a hybrid CNN+RNN network (CRNN), our proposed DSLN yields accuracy improvements of up to 20%. Moreover, DSLN can reduce running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.

Item Type: Article
Uncontrolled Keywords: Internet of things; Network security; RF fingerprint recognition; Deep learning; Low SNR
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Aug 2021 14:42
Last Modified: 23 Sep 2022 19:47

Actions (login required)

View Item View Item