Low-Power Wide-Area Network (LPWAN) technologies, such as LoRa, have gained significant attention for their ability to enable long-range, low-power communication for Internet of Things (IoT) applications. However, the security of LoRa networks remains a major concern, particularly in scenarios where device identification and classification of legitimate and spoofed signals are crucial. This paper studies a deep learning framework to address these challenges, considering LoRa device identification and legitimate vs. rogue LoRa device classification tasks. A deep neural network (DNN), either a convolutional neural network (CNN) or feedforward neural network (FNN), is trained for each task by utilizing real experimental I/Q data for LoRa signals, while rogue signals are generated by using kernel density estimation (KDE) of received signals by rogue devices. Fast Gradient Sign Method (FGSM)-based adversarial attacks are considered for LoRa signal classification tasks using deep learning models. The impact of these attacks is assessed on the performance of two tasks, namely device identification and legitimate vs. rogue device classification, by utilizing separate or common perturbations against these signal classification tasks. Results presented in this paper quantify the level of transferability of adversarial attacks on different LoRa signal classification tasks as a major vulnerability and highlight the need to make IoT applications robust to adversarial attacks.

翻译：低功耗广域网（LPWAN）技术（如LoRa）因能为物联网（IoT）应用实现远距离低功耗通信而受到广泛关注。然而，LoRa网络的安全性仍是重大关切，尤其在设备识别以及合法信号与伪造信号分类至关重要的场景中。本文研究了一种深度学习框架以应对这些挑战，涉及LoRa设备识别及合法与恶意LoRa设备分类任务。通过利用LoRa信号的真实实验I/Q数据训练深度神经网络（DNN），包括卷积神经网络（CNN）或前馈神经网络（FNN），同时基于恶意设备接收信号的核密度估计（KDE）生成恶意信号。针对采用深度学习模型的LoRa信号分类任务，本文考虑了基于快速梯度符号法（FGSM）的对抗攻击。通过分别或联合使用针对这些信号分类任务的扰动，评估了此类攻击对设备识别以及合法与恶意设备分类两项任务性能的影响。本文量化了对抗攻击在不同LoRa信号分类任务间的可迁移性水平——这一主要漏洞，并强调需增强物联网应用对对抗攻击的鲁棒性。