Authors: Yalin E. Sagduyu, Tugba Erpek
Abstract: LoRa provides long-range, energy-efficient communications in Internet of
Things (IoT) applications that rely on Low-Power Wide-Area Network (LPWAN)
capabilities. Despite these merits, concerns persist regarding the security of
LoRa networks, especially in situations where device identification and
authentication are imperative to secure the reliable access to the LoRa
networks. This paper explores a deep learning (DL) approach to tackle these
concerns, focusing on two critical tasks, namely (i) identifying LoRa devices
and (ii) classifying them to legitimate and rogue devices. Deep neural networks
(DNNs), encompassing both convolutional and feedforward neural networks, are
trained for these tasks using actual LoRa signal data. In this setting, the
adversaries may spoof rogue LoRa signals through the kernel density estimation
(KDE) method based on legitimate device signals that are received by the
adversaries. Two cases are considered, (i) training two separate classifiers,
one for each of the two tasks, and (ii) training a multi-task classifier for
both tasks. The vulnerabilities of the resulting DNNs to manipulations in input
samples are studied in form of untargeted and targeted adversarial attacks
using the Fast Gradient Sign Method (FGSM). Individual and common perturbations
are considered against single-task and multi-task classifiers for the LoRa
signal analysis. To provide resilience against such attacks, a defense approach
is presented by increasing the robustness of classifiers with adversarial
training. Results quantify how vulnerable LoRa signal classification tasks are
to adversarial attacks and emphasize the need to fortify IoT applications
against these subtle yet effective threats.
Source: http://arxiv.org/abs/2412.21164v1
