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
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DSLN_Securing_Internet_of_Things_Through_RF_Fingerprint_Recognition_in_Low-SNR_Settings.pdf - Accepted Version Download (1MB) | Preview |
Abstract
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 |
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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: | 30 Mar 2022 15:27 |
URI: | http://repository.essex.ac.uk/id/eprint/30953 |
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