Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection

• URL: http://arxiv.org/abs/2510.09663v1
• Date: Tue, 07 Oct 2025 17:04:50 GMT
• Title: Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection
• Authors: Raju Dhakal, Prashant Shekhar, Laxima Niure Kandel,
• Abstract summary: We propose a Convolutional Neural Network (CNN) based framework for detecting rogue devices and identifying genuine ones.<n>We emulate an attack scenario in which adversaries attempt to mimic the RF characteristics of genuine devices.<n>The proposed approach is verified using IQ samples collected from ten different ADALM-PLUTO Software Defined Radios (SDRs)
• Score: 0.8166364251367626
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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