Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning

LoRa enables long-range, energy-efficient communication for Internet of Things (IoT) applications, making it an essential technology for low-power wide-area networks (LPWANs). However, its security remains a concern, particularly when reliable device identification and authentication are critical. This article addresses these challenges using deep learning (DL) techniques to perform two key tasks: 1) identifying LoRa devices and 2) distinguishing between legitimate signals and rogue signals [generated by kernel density estimation (KDE)]. By training deep neural networks (DNNs) on real LoRa signal data, the study examines the susceptibility of separate models for each task as well as a shared multitask model to untargeted and targeted adversarial attacks, generated with the fast gradient sign method (FGSM). To counter these attacks, a defense strategy using adversarial training is proposed to enhance model robustness. The results highlight vulnerabilities in LoRa security and emphasize the importance of fortifying IoT systems against such sophisticated threats.