A Resilience‐Driven Concept to Manage Drone Intrusions in U‐Space

ABSTRACT With the U‐space revolution, drones are going to reshape both the physical space and the cyberspace of the future urban environment, also with the support of autonomy and artificial intelligence (AI). However, this revolution comes with the cost of new multi‐domain risks, which may be traced back to cyber and physical threats within drone‐based new entrants. A proper assessment and treatment of these risks is essential to achieve the safety and security objectives of U‐space for the drone ecosystem. This will entail further research, especially for the analysis of drone intruders and for the mitigation of the related U‐space impacts. This work proposes a concept for improving the U‐space resilience through a novel AI‐centric service, named DARS (drone attack resilience service), focused on managing unauthorised operations of intruder drones in the physical and cyber domains. DARS‐related threat scenarios and risk‐assessment capabilities are discussed, resorting also to modelling specific drone cyber‐physical attacks. A detailed analysis of DARS AI‐centric functional architecture is provided, with a survey of the potential approaches for intruder trajectory prediction and intent recognition, to be used for the next design stages. Lastly, the work provides a preliminary analysis of how the neutralisation functions could be implemented in DARS.

Authors

Institutions

Publication Details

Journal
IET Radar Sonar & Navigation
Published
2025-01-01
DOI
https://doi.org/10.1049/rsn2.70048
Citations
1
Primary Topic
Adversarial Robustness in Machine Learning
Type
article
Field-Weighted Citation Impact
2.48
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Resilience‐Driven Concept to Manage Drone Intrusions in U‐Space

Domenico Pascarella, Angela Vozella, Albert Remiro Bellostas, Jaime Cabezas Carrasco et al.
1 citations
IET Radar Sonar & Navigation
Adversarial Robustness in Machine Learning
2.48
article

A Resilience‐Driven Concept to Manage Drone Intrusions in U‐Space

Domenico Pascarella, Angela Vozella, Albert Remiro Bellostas, Jaime Cabezas Carrasco, Gabriella Gigante, Thomas Dubot, Pierre Bieber
article en
1 citations

Abstract

ABSTRACT With the U‐space revolution, drones are going to reshape both the physical space and the cyberspace of the future urban environment, also with the support of autonomy and artificial intelligence (AI). However, this revolution comes with the cost of new multi‐domain risks, which may be traced back to cyber and physical threats within drone‐based new entrants. A proper assessment and treatment of these risks is essential to achieve the safety and security objectives of U‐space for the drone ecosystem. This will entail further research, especially for the analysis of drone intruders and for the mitigation of the related U‐space impacts. This work proposes a concept for improving the U‐space resilience through a novel AI‐centric service, named DARS (drone attack resilience service), focused on managing unauthorised operations of intruder drones in the physical and cyber domains. DARS‐related threat scenarios and risk‐assessment capabilities are discussed, resorting also to modelling specific drone cyber‐physical attacks. A detailed analysis of DARS AI‐centric functional architecture is provided, with a survey of the potential approaches for intruder trajectory prediction and intent recognition, to be used for the next design stages. Lastly, the work provides a preliminary analysis of how the neutralisation functions could be implemented in DARS.

IET Radar Sonar & NavigationVol. 19(1)
Université Fédérale de Toulouse Midi-Pyrénées (FR), Italian Aerospace Research Centre (IT), Instituto Nacional de Técnica Aeroespacial (ES)
Sustainable cities and communities
Openalex Percentile: Top 9%
Adversarial Robustness in Machine Learning
2.48
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.