Intrusion Into RF Fingerprint Authorized Wireless Communications With Generative-Adversarial-Network-Based Attackers

Abstract

Radio Frequency Fingerprints (RFFs) imprinted on the RF signals by imperfections of hardware manufacturing enable device authorization augmentation in the Internet of Things (IoT) communication. However, the identifiable RF fingerprints are exposed to potential adversary attackers that can mimic the identifiable RF fingerprints of the authorized devices and fool the classifier at the receiver to obstruct secure communications in an open environment. This work proposes an attacker based on a Generative Adversarial Network (GAN) with a Variational Autoencoder (VAE) to extract the identifiable RF fingerprints generated from over 220 authorized transmitters. At the receiver, a Convolutional-Neural-Network-(CNN)-based classifier is deployed and shows a multi-class False Positive Rate (FPR) of 97.7% on the signals synthesized by the attacker at 30 dB Signal-to-Noise Ratio (SNR). In dealing with secure communication featuring time-varying RFFs, it’s essential to also monitor the time required for attackers to adapt. Within 60 seconds, results indicate that there’s a 92.9% probability of deceiving the receiver at 30 dB, with the possibility remaining at 87.4%s even when the SNR drops to 5 dB, which shows the attacker’s capability to overcome a wide range of SNR conditions. Furthermore, an evaluation of the distance effect highlights its robustness to device movement.