# Adversarial Attacks on LoRa Device Identification and Rogue Signal Detection with Deep Learning

arXiv: https://arxiv.org/abs/2312.16715 · HTML: https://mlsanity.com/paper/2312.16715/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 26 references that are in the map.
- First posted on arXiv 27 Dec 2023.

## Builds on (most cited first, 14 of 14)

- [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)
- [Adversarial Attacks on Deep-Learning Based Radio Signal Classification](https://mlsanity.com/paper/1808.07713/) (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)
- [Channel Effects on Surrogate Models of Adversarial Attacks against Wireless Signal Classifiers](https://mlsanity.com/paper/2012.02160/) (2020)

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

- [Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning](https://mlsanity.com/paper/2412.21164/) (2024)

## Abstract (arXiv)

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.