Deep Learning-Based Wireless Signal Identification for IoT Device Security

Abstract

In this paper, the signal identification algorithms are outlined based on deep learning to increase the security of the Internet of Things (IoT) devices. Using raw in-phase and quadrature (I/Q) signal representations, the offered framework can learn characteristic radio-frequency fingerprints that can unambiguously identify legitimate IoT devices, even when subjected to changing channel conditions. Convolutional and recurrent neural networks are compared on the basis of their capability to identify devices and spoofing, impersonation, and rogue transmitters, without involving the use of cryptographic keys. Diverse experiments with simulated and real-world wireless data show that it has a high identification accuracy, resistance to noise, and resistance to prevalent wireless attacks. The findings indicate that deep learning is considerably superior to the traditional methods based on features and statistics, especially in the low signal-to-noise settings. This paper demonstrates the practicality of having smart signal detection as part of lightweight IoT security protocols, which allow uninterrupted, passive, and scalable authentication of next-generation wireless networks and that allow practical implementation in heterogeneous IoT environments.