Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection
This work addresses security in device authentication for wireless communication systems, but it is incremental as it combines existing CNN and GAN methods for a specific attack scenario.
The paper tackled the problem of rogue transmitter detection in Radio Frequency Fingerprinting by proposing a CNN-based framework with softmax probability thresholding, achieving verification using IQ samples from ten SDRs with seven genuine and two rogue devices.
Radio Frequency Fingerprinting (RFF) has evolved as an effective solution for authenticating devices by leveraging the unique imperfections in hardware components involved in the signal generation process. In this work, we propose a Convolutional Neural Network (CNN) based framework for detecting rogue devices and identifying genuine ones using softmax probability thresholding. We emulate an attack scenario in which adversaries attempt to mimic the RF characteristics of genuine devices by training a Generative Adversarial Network (GAN) using In-phase and Quadrature (IQ) samples from genuine devices. The proposed approach is verified using IQ samples collected from ten different ADALM-PLUTO Software Defined Radios (SDRs), with seven devices considered genuine, two as rogue, and one used for validation to determine the threshold.