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

arXiv: https://arxiv.org/abs/2412.21164 · HTML: https://mlsanity.com/paper/2412.21164/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 46 references that are in the map.
- First posted on arXiv 30 Dec 2024.

## Builds on (most cited first, 25 of 25)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Deep Learning Models Resistant to Adversarial Attacks](https://mlsanity.com/paper/1706.06083/) (2017)
- [Certified Adversarial Robustness via Randomized Smoothing](https://mlsanity.com/paper/1902.02918/) (2019)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Transferability in Machine Learning: from Phenomena to Black-Box Attacks using Adversarial Samples](https://mlsanity.com/paper/1605.07277/) (2016)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (2018)
- [A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security](https://mlsanity.com/paper/1807.11023/) (2018)
- [Deep Learning for Wireless Communications](https://mlsanity.com/paper/2005.06068/) (2020)
- [Deep Architectures for Modulation Recognition](https://mlsanity.com/paper/1703.09197/) (2017)
- [Deep Learning for RF Signal Classification in Unknown and Dynamic Spectrum Environments](https://mlsanity.com/paper/1909.11800/) (2019)
- [Channel-Aware Adversarial Attacks Against Deep Learning-Based Wireless Signal Classifiers](https://mlsanity.com/paper/2005.05321/) (2020)
- [Adversarial Machine Learning in Wireless Communications using RF Data: A Review](https://mlsanity.com/paper/2012.14392/) (2020)
- [Generative Adversarial Network for Wireless Signal Spoofing](https://mlsanity.com/paper/1905.01008/) (2019)
- [Multi-task Learning Approach for Automatic Modulation and Wireless Signal Classification](https://mlsanity.com/paper/2101.10254/) (2021)
- [Towards Scalable and Channel-Robust Radio Frequency Fingerprint Identification for LoRa](https://mlsanity.com/paper/2107.02867/) (2021)
- [Generative Adversarial Network in the Air: Deep Adversarial Learning for Wireless Signal Spoofing](https://mlsanity.com/paper/2007.08363/) (2020)
- [When Wireless Security Meets Machine Learning: Motivation, Challenges, and Research Directions](https://mlsanity.com/paper/2001.08883/) (2020)
- [A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges](https://mlsanity.com/paper/2202.11082/) (2022)
- [Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers](https://mlsanity.com/paper/2012.02160/) (2020)
- [Joint Sensing and Semantic Communications with Multi-Task Deep Learning](https://mlsanity.com/paper/2311.05017/) (2023)
- [Multi-Receiver Task-Oriented Communications via Multi-Task Deep Learning](https://mlsanity.com/paper/2308.06884/) (2023)
- [Continual Deep Reinforcement Learning to Prevent Catastrophic Forgetting in Jamming Mitigation](https://mlsanity.com/paper/2410.10521/) (2024)
- [White-Box Adversarial Attacks on Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2308.07433/) (2023)
- [Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning](https://mlsanity.com/paper/2312.16715/) (2023)

## Built on by (most cited first, 1 of 1)

- [Adversarial Attacks Against Deep Learning-Based Radio Frequency Fingerprint Identification](https://mlsanity.com/paper/2512.12002/) (2025)

## Abstract (arXiv)

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.