A Lightweight AI-Based Approach for Drone Jamming Detection

Sergio Cibecchini (), Francesco Chiti () and Laura Pierucci
Additional contact information
Sergio Cibecchini: Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50139 Florence, Italy
Francesco Chiti: Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50139 Florence, Italy
Laura Pierucci: Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50139 Florence, Italy

Future Internet, 2025, vol. 17, issue 1, 1-23

Abstract: The future integration of drones in 6G networks will significantly enhance their capabilities, enabling a wide range of new applications based on autonomous operation. However, drone networks are particularly vulnerable to jamming attacks, a type of availability attack that can disrupt network operation and hinder drone functionality. In this paper, we propose a low complexity unsupervised machine learning approach for the detection of constant and periodic jamming attacks, using the Isolation Forest algorithm. We detail the tuning of the base model as well as the integration with a Majority Rule module which significantly reduced the number of false positives caused by environmental noise, achieving high accuracy and precision. Our approach outperforms the standard Isolation Forest model in the detection of both constant and periodic jamming attacks, while still correctly identifying nominal traffic. Finally, we discuss the potential integration of the proposed solution in 6G-enabled drone networks, as a lightweight edge-based solution for enhancing security against jamming attacks.

Keywords: jamming attacks; 6G drone networks; isolation forest; edge AI; IoT security (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1999-5903/17/1/14/pdf (application/pdf)
https://www.mdpi.com/1999-5903/17/1/14/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:17:y:2025:i:1:p:14-:d:1559544

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().