Wu, Weiwei and Hu, Su and Lin, Di and Liu, Zilong (2021) 'DSLN: Securing Internet of Things Through RF Fingerprint Recognition in Low-SNR Settings.' IEEE Internet of Things Journal. ISSN 2327-4662

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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
Uncontrolled Keywords:	Internet of things, Network security, RF fingerprint recognition, Deep learning, Low SNR
Divisions:	Faculty of Science and Health > Computer Science and Electronic Engineering, School of
Depositing User:	Elements
Date Deposited:	24 Aug 2021 14:42
Last Modified:	24 Aug 2021 14:42
URI:	http://repository.essex.ac.uk/id/eprint/30953

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