  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.
