A Drone Early Warning System (DEWS) for Predicting Threatening Trajectories

Tonmoay Deb; Sven De Laaf; Valerio La Gatta; Odette Lemmens; Roy Lindelauf; Max Van Meerten; Herwin Meerveld; Afke Neeleman; Marco Postiglione; V. S. Subrahmanian

doi:10.1109/MIS.2025.3540003

A Drone Early Warning System (DEWS) for Predicting Threatening Trajectories

Tonmoay Deb, Sven De Laaf, Valerio La Gatta, Odette Lemmens, Roy Lindelauf, Max Van Meerten, Herwin Meerveld, Afke Neeleman, Marco Postiglione^*, V. S. Subrahmanian

^*Corresponding author for this work

Computer Science

Research output: Contribution to journal › Article › peer-review

Abstract

Over the last few years, there has been increasing use of drones by terror groups and in armed conflict. Several technologies have been developed to detect drone flights. However, much less work has been done on the Drone Threat Prediction Problem (DTPP): predicting which drone trajectories are threatening and which ones are not. We propose DEWS (Drone Early Warning System), a framework to solve this problem. Solving DTPP early is key. Once a drone starts on its trajectory, we show that DEWS can make accurate predictions within 20-30 seconds of the flight with an F1-score of over 80% on data about a major European city. We study the tradeoff between earliness of predictions and accuracy. We identify the key features that ensure good predictions.

Original language	English (US)
Journal	IEEE Intelligent Systems
DOIs	https://doi.org/10.1109/MIS.2025.3540003
State	Accepted/In press - 2025

ASJC Scopus subject areas

Computer Networks and Communications
Artificial Intelligence

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10.1109/MIS.2025.3540003

Cite this

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title = "A Drone Early Warning System (DEWS) for Predicting Threatening Trajectories",

abstract = "Over the last few years, there has been increasing use of drones by terror groups and in armed conflict. Several technologies have been developed to detect drone flights. However, much less work has been done on the Drone Threat Prediction Problem (DTPP): predicting which drone trajectories are threatening and which ones are not. We propose DEWS (Drone Early Warning System), a framework to solve this problem. Solving DTPP early is key. Once a drone starts on its trajectory, we show that DEWS can make accurate predictions within 20-30 seconds of the flight with an F1-score of over 80% on data about a major European city. We study the tradeoff between earliness of predictions and accuracy. We identify the key features that ensure good predictions.",

author = "Tonmoay Deb and {De Laaf}, Sven and {La Gatta}, Valerio and Odette Lemmens and Roy Lindelauf and {Van Meerten}, Max and Herwin Meerveld and Afke Neeleman and Marco Postiglione and Subrahmanian, {V. S.}",

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doi = "10.1109/MIS.2025.3540003",

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journal = "IEEE Intelligent Systems",

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AU - Deb, Tonmoay

AU - De Laaf, Sven

AU - La Gatta, Valerio

AU - Lemmens, Odette

AU - Lindelauf, Roy

AU - Van Meerten, Max

AU - Meerveld, Herwin

AU - Neeleman, Afke

AU - Postiglione, Marco

AU - Subrahmanian, V. S.

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A Drone Early Warning System (DEWS) for Predicting Threatening Trajectories

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ASJC Scopus subject areas

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