# Unauthorized Drone Detection: Experiments and Prototypes

arXiv: https://arxiv.org/abs/2212.01436 · HTML: https://mlsanity.com/paper/2212.01436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 25 references that are in the map.
- First posted on arXiv 2 Dec 2022.

## Builds on (most cited first, 5 of 5)

- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [Empirical Study of Drone Sound Detection in Real-Life Environment with Deep Neural Networks](https://mlsanity.com/paper/1701.05779/) (2017)
- [Drone classification from RF fingerprints using deep residual nets](https://mlsanity.com/paper/2011.13663/) (2020)
- [Micro-UAV Detection and Classification from RF Fingerprints Using Machine Learning Techniques](https://mlsanity.com/paper/1901.07703/) (2019)
- [Amateur Drones Detection: A machine learning approach utilizing the acoustic signals in the presence of strong interference](https://mlsanity.com/paper/2003.01519/) (2020)

## Abstract (arXiv)

The increase in the number of unmanned aerial vehicles a.k.a. drones pose several threats to public privacy, critical infrastructure and cyber security. Hence, detecting unauthorized drones is a significant problem which received attention in the last few years. In this paper, we present our experimental work on three drone detection methods (i.e., acoustic detection, radio frequency (RF) detection, and visual detection) to evaluate their efficacy in both indoor and outdoor environments. Owing to the limitations of these schemes, we present a novel encryption-based drone detection scheme that uses a two-stage verification of the drone's received signal strength indicator (RSSI) and the encryption key generated from the drone's position coordinates to reliably detect an unauthorized drone in the presence of authorized drones.