A Lightweight AI-Based Approach for Drone Jamming Detection
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 opera...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/1/14 |
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| author | Sergio Cibecchini Francesco Chiti Laura Pierucci |
| author_facet | Sergio Cibecchini Francesco Chiti Laura Pierucci |
| author_sort | Sergio Cibecchini |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-1758a1badca74507971efae144a2aff3 |
| institution | Kabale University |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-1758a1badca74507971efae144a2aff32025-01-24T13:33:34ZengMDPI AGFuture Internet1999-59032025-01-011711410.3390/fi17010014A Lightweight AI-Based Approach for Drone Jamming DetectionSergio Cibecchini0Francesco Chiti1Laura Pierucci2Dipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50139 Florence, ItalyDipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50139 Florence, ItalyDipartimento di Ingegneria dell’Informazione, Università degli Studi di Firenze, 50139 Florence, ItalyThe 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.https://www.mdpi.com/1999-5903/17/1/14jamming attacks6G drone networksisolation forestedge AIIoT security |
| spellingShingle | Sergio Cibecchini Francesco Chiti Laura Pierucci A Lightweight AI-Based Approach for Drone Jamming Detection Future Internet jamming attacks 6G drone networks isolation forest edge AI IoT security |
| title | A Lightweight AI-Based Approach for Drone Jamming Detection |
| title_full | A Lightweight AI-Based Approach for Drone Jamming Detection |
| title_fullStr | A Lightweight AI-Based Approach for Drone Jamming Detection |
| title_full_unstemmed | A Lightweight AI-Based Approach for Drone Jamming Detection |
| title_short | A Lightweight AI-Based Approach for Drone Jamming Detection |
| title_sort | lightweight ai based approach for drone jamming detection |
| topic | jamming attacks 6G drone networks isolation forest edge AI IoT security |
| url | https://www.mdpi.com/1999-5903/17/1/14 |
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