FAN-GHETS24: A Flying Ad Hoc Network Dataset for Early Time Series Classification of Grey Hole Attacks

Hutchins, Charles (2024) FAN-GHETS24: A Flying Ad Hoc Network Dataset for Early Time Series Classification of Grey Hole Attacks. Zenodo doi:10.5281/zenodo.13315419 [Dataset]

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 defense to preserve the network's quality of service. This novel dataset, FAN-GHETS24, is designed for early time series classification of various grey hole attack scenarios. The dataset is derived from sequences of packet interactions between UAVs within the network, generated through multiple simulations. 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 universal deployment across UAVs; and secondly, the application of feature engineering techniques to format the data for machine learning model integration. This dataset supports the thesis titled: "An investigation of defence protocols for the mitigation of grey hole attacks in flying ad hoc networks"

This record has no associated files available for download.

Contributors

Research team head: Leonardo Aniello

Research team head: Basel Halak

Research team head: Enrico Gerding

Download statistics