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

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 the running time by up to 60%, indicating its great potential to a real-time IoT system, e.g., an intelligent automotive system.