A flying ad-hoc network dataset for early time series classification of grey hole attacks
A flying ad-hoc network dataset for early time series classification of grey hole attacks
Flying ad-hoc networks (FANETs) consist of multiple unmanned aerial vehicles (UAVs) that rely on multi-hop routes for communication. These routes are particularly susceptible to grey hole attacks, necessitating swift and accurate defence to preserve the network’s quality of service. Grey hole attacks are a type of denial-of-service attack where malicious nodes selectively drop packets, disrupting the normal flow of data in the network. This paper introduces and motivates a novel dataset, FAN-GHETS24, designed for the fast classification of various grey hole attacks. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations of FANETs. These sequences undergo post-processing via two methods: firstly, an anonymization procedure that replaces IP addresses with standard string variables, allowing for offline model training and deployment on any UAV; and secondly, the application of feature engineering techniques to format the data for machine learning model integration. The dataset’s utility is validated using a time series classification model which focuses on classifying grey hole attacks as quickly as possible.
Hutchins, Charles
560e5055-1f19-4041-b6af-77f26ad51b94
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
15 August 2025
Hutchins, Charles
560e5055-1f19-4041-b6af-77f26ad51b94
Aniello, Leonardo
9846e2e4-1303-4b8b-9092-5d8e9bb514c3
Gerding, Enrico
d9e92ee5-1a8c-4467-a689-8363e7743362
Halak, Basel
8221f839-0dfd-4f81-9865-37def5f79f33
Hutchins, Charles, Aniello, Leonardo, Gerding, Enrico and Halak, Basel
(2025)
A flying ad-hoc network dataset for early time series classification of grey hole attacks.
Scientific Data, 12 (1), [1431].
(doi:10.1038/s41597-025-05560-1).
Abstract
Flying ad-hoc networks (FANETs) consist of multiple unmanned aerial vehicles (UAVs) that rely on multi-hop routes for communication. These routes are particularly susceptible to grey hole attacks, necessitating swift and accurate defence to preserve the network’s quality of service. Grey hole attacks are a type of denial-of-service attack where malicious nodes selectively drop packets, disrupting the normal flow of data in the network. This paper introduces and motivates a novel dataset, FAN-GHETS24, designed for the fast classification of various grey hole attacks. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations of FANETs. These sequences undergo post-processing via two methods: firstly, an anonymization procedure that replaces IP addresses with standard string variables, allowing for offline model training and deployment on any UAV; and secondly, the application of feature engineering techniques to format the data for machine learning model integration. The dataset’s utility is validated using a time series classification model which focuses on classifying grey hole attacks as quickly as possible.
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Accepted/In Press date: 7 July 2025
Published date: 15 August 2025
Additional Information:
© 2025. The Author(s).
Identifiers
Local EPrints ID: 503975
URI: http://eprints.soton.ac.uk/id/eprint/503975
ISSN: 2052-4463
PURE UUID: eb44e622-3fea-43b4-9771-a4eefdd7699a
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Date deposited: 19 Aug 2025 17:05
Last modified: 28 Aug 2025 02:03
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