用CNN与GAN对抗检测伪基站，靠硬件指纹区分真伪设备
Adversarial-Resilient RF Fingerprinting: A CNN-GAN Framework for Rogue Transmitter Detection
- 用CNN分析射频信号的硬件指纹，结合软阈值判断设备真伪
- 在7个真设备、2个伪设备上测试，识别准确率达98.6%
- 首次模拟攻击者用GAN模仿真设备，验证系统抗伪造能力
射频指纹技术通过信号生成过程中硬件组件的固有差异，为设备认证提供有效方案。本文提出一种基于卷积神经网络（CNN）的框架，利用软最大概率阈值检测非法设备并识别真实设备。我们模拟攻击场景：攻击者使用真实设备的正交采样（IQ）数据训练生成对抗网络（GAN），试图模仿其射频特征。实验基于10个ADALM-PLUTO软件定义无线电采集的IQ样本，其中7个为真实设备，2个为伪造设备，1个用于确定阈值。结果表明该方法在对抗性攻击下仍能保持高识别精度。
原文摘要 · Abstract (English)
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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