With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure generation process is initially employed to transform RF fingerprint features from time-domain waveforms to two-dimensional figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A universal software radio peripheral receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.

With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A Universal Software Radio Peripheral (USRP) receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95%identification accuracy in a real environment.

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With the dramatic development of the internet of things (IoT), security issues such as identity authentication have received serious attention. The radio frequency (RF) fingerprint of IoT device is an inherent feature, which can hardly be imitated. In this paper, we propose a rogue device identification technique via RF fingerprinting using deep learning-based generative adversarial network (GAN). Being different from traditional classification problems in RF fingerprint identifications, this work focuses on unknown accessing device recognition without prior information. A differential constellation trace figure (DCTF) generation process is initially employed to transform RF fingerprint features from time-domain waveforms to 2-dimensional (2D) figures. Then, by using GAN, which is a kind of unsupervised learning algorithm, we can discriminate rogue devices without any prior information. An experimental verification system is built with 54 ZigBee devices regarded as recognized devices and accessing devices. A USRP receiver is used to capture the signal and identify the accessing devices. Experimental results show that the proposed rogue device identification method can achieve 95% identification accuracy in a real environment.