标题： VeriPHY：5G环境中无线通信的物理层信号认证 显示英文标题

标题： VeriPHY: Physical Layer Signal Authentication for Wireless Communication in 5G Environments

摘要： 物理层认证（PLA）利用通信介质的固有特性，在无线网络中提供安全且高效的认证，无需传统的加密方法。 随着深度学习的进步，PLA因其准确性和可靠性而成为广泛采用的技术。 在本文中，我们介绍了VeriPHY，这是一种基于深度学习的5G网络新型PLA解决方案，通过使用隐写术在无线I/Q传输中嵌入签名来实现设备的唯一识别。 VeriPHY通过从高斯混合模型中采样连续生成伪随机签名，其分布经过精心调整以确保签名随时间的唯一性和隐蔽性，然后将新生成的签名嵌入用户发送到5G gNB的I/Q样本中。 利用深度神经网络，VeriPHY根据这些嵌入的签名来识别和认证用户。 VeriPHY实现了高精度，在签名每20毫秒更新一次时，识别唯一签名的准确率在93%到100%之间，误报率低，推理时间为28毫秒。 此外，我们还演示了一种隐蔽生成模式，其中签名的生成方式使得它们几乎无法与未修改的5G信号区分开来，同时保持超过93%的检测准确率。 显示英文摘要

摘要： Physical layer authentication (PLA) uses inherent characteristics of the communication medium to provide secure and efficient authentication in wireless networks, bypassing the need for traditional cryptographic methods. With advancements in deep learning, PLA has become a widely adopted technique for its accuracy and reliability. In this paper, we introduce VeriPHY, a novel deep learning-based PLA solution for 5G networks, which enables unique device identification by embedding signatures within wireless I/Q transmissions using steganography. VeriPHY continuously generates pseudo-random signatures by sampling from Gaussian Mixture Models whose distribution is carefully varied to ensure signature uniqueness and stealthiness over time, and then embeds the newly generated signatures over I/Q samples transmitted by users to the 5G gNB. Utilizing deep neural networks, VeriPHY identifies and authenticates users based on these embedded signatures. VeriPHY achieves high precision, identifying unique signatures between 93% and 100% with low false positive rates and an inference time of 28 ms when signatures are updated every 20 ms. Additionally, we also demonstrate a stealth generation mode where signatures are generated in a way that makes them virtually indistinguishable from unaltered 5G signals while maintaining over 93% detection accuracy.