Cyber-physical systems have recently been used in several areas (such as connected and autonomous vehicles) due to their high maneuverability. On the other hand, they are susceptible to cyber-attacks. Radio frequency (RF) fingerprinting emerges as a promising approach. This work aims to analyze the impact of decoupling tapped delay line and clustered delay line (TDL+CDL) augmentation-driven deep learning (DL) on transmitter-specific fingerprints to discriminate malicious users from legitimate ones. This work also considers 5G-only-CDL, WiFi-only-TDL augmentation approaches. RF fingerprinting models are sensitive to changing channels and environmental conditions. For this reason, they should be considered during the deployment of a DL model. Data acquisition can be another option. Nonetheless, gathering samples under various conditions for a train set formation may be quite hard. Consequently, data acquisition may not be feasible. This work uses a dataset that includes 5G, 4G, and WiFi samples, and it empowers a CDL+TDL-based augmentation technique in order to boost the learning performance of the DL model. Numerical results show that CDL+TDL, 5G-only-CDL, and WiFi-only-TDL augmentation approaches achieve 87.59%, 81.63%, 79.21% accuracy on unobserved data while TDL/CDL augmentation technique and no augmentation approach result in 77.81% and 74.84% accuracy on unobserved data, respectively.

翻译：信息物理系统因其高机动性，近年来已被应用于多个领域（如网联自动驾驶车辆）。然而，此类系统易受网络攻击威胁。射频指纹识别作为一种具有前景的解决方案应运而生。本研究旨在分析解耦抽头延迟线+簇延迟线增强驱动深度学习对发射器特定指纹的影响，以区分恶意用户与合法用户。同时，本研究还探讨了仅用5G-CDL、仅用WiFi-TDL的数据增强方法。射频指纹模型对信道变化和环境条件敏感，因此部署深度学习模型时需考虑这些因素。数据采集是另一可行方案，但为训练集采集多种条件下的样本极为困难，故实际中数据采集可能不可行。本研究采用包含5G、4G和WiFi样本的数据集，并引入基于CDL+TDL的增强技术以提升深度学习模型的学习性能。数值结果表明，CDL+TDL、仅5G-CDL和仅WiFi-TDL增强方法在未观测数据上的准确率分别为87.59%、81.63%和79.21%，而TDL/CDL增强技术及无增强方法在未观测数据上的准确率分别为77.81%和74.84%。