# Data-driven classification of low-power communication signals by an unauthenticated user using a software-defined radio

arXiv: https://arxiv.org/abs/2309.04088 · HTML: https://mlsanity.com/paper/2309.04088/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 8 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends](https://mlsanity.com/paper/1505.07919/) (2015)
- [Understanding the limits of LoRaWAN](https://mlsanity.com/paper/1607.08011/) (2016)
- [Selective Jamming of LoRaWAN using Commodity Hardware](https://mlsanity.com/paper/1712.02141/) (2017)
- [A Configurable Mathematical Model for Single-Gateway LoRaWAN Performance Analysis](https://mlsanity.com/paper/1912.01285/) (2019)
- [Robust remote estimation over the collision channel in the presence of an intelligent jammer](https://mlsanity.com/paper/2204.00148/) (2022)
- [Robust one-shot estimation over shared networks in the presence of denial-of-service attacks](https://mlsanity.com/paper/2302.14689/) (2023)

## Abstract (arXiv)

Many large-scale distributed multi-agent systems exchange information over low-power communication networks. In particular, agents intermittently communicate state and control signals in robotic network applications, often with limited power over an unlicensed spectrum, prone to eavesdropping and denial-of-service attacks. In this paper, we argue that a widely popular low-power communication protocol known as LoRa is vulnerable to denial-of-service attacks by an unauthenticated attacker if it can successfully identify a target signal's bandwidth and spreading factor. Leveraging a structural pattern in the LoRa signal's instantaneous frequency representation, we relate the problem of jointly inferring the two unknown parameters to a classification problem, which can be efficiently implemented using neural networks.