Drone-Mag: UAV Identification and Authentication via Electromagnetic Emissions

Unmanned Aerial Vehicles (UAVs) are gaining increased popularity in a wide range of domains and applications. As a result, they are also becoming a target of malicious attacks. For example, drone impersonation of military or civilian drones can cause serious security and privacy breaches. There have been some recent contributions that aim to integrate digital certificates as an authentication tool for drones, but such software techniques are often defenseless against physical compromise. In this article, to the best of our knowledge, we are the first to propose a physical layer drone authentication framework to augment existing multifactor authentication schemes leveraging the unintentional Electromagnetic (EM) emissions of the drone’s electronic components. Our solution, Drone-Mag , exploits the inherent non-idealities and imperfections present in drones’ electronic integrated circuits that are introduced during their manufacturing process. Those emissions are hard to mimic or replicate, providing a robust basis for drone authentication. Drone-Mag is a passive, non-interactive, and privacy-preserving authentication solution and does not require software or hardware modifications to available drones. We test the performance of Drone-Mag focusing on the unintentional EM emissions of 23 drones. In particular, we addressed three main tasks: (i) identification of 14 different drones and flight controllers; (ii) authentication of 10 identical (same brand and model) drones; and (iii) rogue drone detection using autoencoders. All the listed tasks achieve a minimum average of 0.97 F1-score, showing the viability and efficiency of the proposed authentication method.

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Publication Details

Journal
ACM Transactions on Cyber-Physical Systems
Published
2025-04-21
DOI
https://doi.org/10.1145/3731565
Citations
2
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
Field-Weighted Citation Impact
4.54
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article

Drone-Mag: UAV Identification and Authentication via Electromagnetic Emissions

Omar Adel Ibrahim, Roberto Di Pietro
2 citations
ACM Transactions on Cyber-Physical Systems
Physical Unclonable Functions (PUFs) and Hardware Security
4.54
article

Drone-Mag: UAV Identification and Authentication via Electromagnetic Emissions

Omar Adel Ibrahim, Roberto Di Pietro
article en
2 citations

Abstract

Unmanned Aerial Vehicles (UAVs) are gaining increased popularity in a wide range of domains and applications. As a result, they are also becoming a target of malicious attacks. For example, drone impersonation of military or civilian drones can cause serious security and privacy breaches. There have been some recent contributions that aim to integrate digital certificates as an authentication tool for drones, but such software techniques are often defenseless against physical compromise. In this article, to the best of our knowledge, we are the first to propose a physical layer drone authentication framework to augment existing multifactor authentication schemes leveraging the unintentional Electromagnetic (EM) emissions of the drone’s electronic components. Our solution, Drone-Mag , exploits the inherent non-idealities and imperfections present in drones’ electronic integrated circuits that are introduced during their manufacturing process. Those emissions are hard to mimic or replicate, providing a robust basis for drone authentication. Drone-Mag is a passive, non-interactive, and privacy-preserving authentication solution and does not require software or hardware modifications to available drones. We test the performance of Drone-Mag focusing on the unintentional EM emissions of 23 drones. In particular, we addressed three main tasks: (i) identification of 14 different drones and flight controllers; (ii) authentication of 10 identical (same brand and model) drones; and (iii) rogue drone detection using autoencoders. All the listed tasks achieve a minimum average of 0.97 F1-score, showing the viability and efficiency of the proposed authentication method.

ACM Transactions on Cyber-Physical SystemsVol. 9(3)
King Abdullah University of Science and Technology (SA)
Openalex Percentile: Top 7%
Physical Unclonable Functions (PUFs) and Hardware Security
4.54
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