2AS-DS: Anonymous Authentication Scheme Based on Physical Unclonable Function for Drone Swarms

Drone swarms involve multiple drones working together to accomplish tasks beyond the capability of single drones. Effectively detecting, tracking, and managing the anticipated increase in air traffic is growing a significant challenge. Drone traffic management must adhere to regulations and spatiotemporal limitations, such as no-fly zones or zones with restricted flight times. The Federal Aviation Administration (FAA) recently established a new rule to enhance drone accountability. This standard mandates that drone broadcast messages disclosing identity over the wireless channel. This requirement raises security and privacy issues for drones, as unauthorized individuals can identify drones in a given area, potentially leading to attacks. Additionally, due to the large number of drones in each swarm, it is not optimal for each drone to contact the control server directly. In this work, we propose anonymous authentication scheme using PUF (Physically Unclonable Functions), aimed at ensuring security and efficiency for drone swarms to verify their access authority (named 2AS-DS). 2AS-DS is designed to fully comply with FAA regulations while ensuring anonymity and untraceability of drone broadcast messages. Only the Trusted Authority (e.g., the control server) can reveal the drone's long-term identity. Security analyses demonstrate that 2AS-DS is secure, interoperable and scalable. Therefore, it may be useful for drone swarm applications.

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Publication Details

Published
2024-11-03
DOI
https://doi.org/10.1109/edis63605.2024.10783195
Citations
2
Primary Topic
Physical Unclonable Functions (PUFs) and Hardware Security
Type
article
Field-Weighted Citation Impact
0.98
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article

2AS-DS: Anonymous Authentication Scheme Based on Physical Unclonable Function for Drone Swarms

Ilyes Ahmim, Fériel Bouakkaz, Nacira Ghoualmi‐Zine, Abderrezak Rachedi
2 citations
Physical Unclonable Functions (PUFs) and Hardware Security
0.98
article

2AS-DS: Anonymous Authentication Scheme Based on Physical Unclonable Function for Drone Swarms

Ilyes Ahmim, Fériel Bouakkaz, Nacira Ghoualmi‐Zine, Abderrezak Rachedi
article en
2 citations

Abstract

Drone swarms involve multiple drones working together to accomplish tasks beyond the capability of single drones. Effectively detecting, tracking, and managing the anticipated increase in air traffic is growing a significant challenge. Drone traffic management must adhere to regulations and spatiotemporal limitations, such as no-fly zones or zones with restricted flight times. The Federal Aviation Administration (FAA) recently established a new rule to enhance drone accountability. This standard mandates that drone broadcast messages disclosing identity over the wireless channel. This requirement raises security and privacy issues for drones, as unauthorized individuals can identify drones in a given area, potentially leading to attacks. Additionally, due to the large number of drones in each swarm, it is not optimal for each drone to contact the control server directly. In this work, we propose anonymous authentication scheme using PUF (Physically Unclonable Functions), aimed at ensuring security and efficiency for drone swarms to verify their access authority (named 2AS-DS). 2AS-DS is designed to fully comply with FAA regulations while ensuring anonymity and untraceability of drone broadcast messages. Only the Trusted Authority (e.g., the control server) can reveal the drone's long-term identity. Security analyses demonstrate that 2AS-DS is secure, interoperable and scalable. Therefore, it may be useful for drone swarm applications.

Badji Mokhtar-Annaba University (DZ), Université Paris-Panthéon-Assas (FR), Centre National de la Recherche Scientifique (FR), Laboratoire d'Informatique Gaspard-Monge (FR), Université Gustave Eiffel (FR)
Openalex Percentile: Top 23%
Physical Unclonable Functions (PUFs) and Hardware Security
0.98
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2AS-DS: Anonymous Authentication Scheme Based on Physical Unclonable Function for Drone Swarms — Ilyes Ahmim, Fériel Bouakkaz, et al. · (2024) | TGRS Research Map | TGRS