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Hornet 40: Network dataset of geographically placed honeypots

Deception technologies, and honeypots in particular, have been used for decades to understand how cyber attacks and attackers work. A myriad of factors impact the effectiveness of a honeypot. However, very few is known about the impact of the geographical location of honeypots on the amount and type...

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Detalles Bibliográficos
Autores principales: Valeros, Veronica, Garcia, Sebastian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749222/
https://www.ncbi.nlm.nih.gov/pubmed/35036492
http://dx.doi.org/10.1016/j.dib.2022.107795
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author Valeros, Veronica
Garcia, Sebastian
author_facet Valeros, Veronica
Garcia, Sebastian
author_sort Valeros, Veronica
collection PubMed
description Deception technologies, and honeypots in particular, have been used for decades to understand how cyber attacks and attackers work. A myriad of factors impact the effectiveness of a honeypot. However, very few is known about the impact of the geographical location of honeypots on the amount and type of attacks. Hornet 40 is the first dataset designed to help understand how the geolocation of honeypots may impact the inflow of network attacks. The data consists of network flows in binary and text format, with up to 118 features, including 480 bytes of the content of each flow. They were created using the Argus flow collector. The passive honeypots are IP addresses connected to the Internet and do not have any honeypot software running, so attacks are not interactive. The data was collected from identically configured honeypot servers in eight locations: Amsterdam, Bangalore, Frankfurt, London, New York, San Francisco, Singapore, and Toronto. The dataset contains over 4.7 million network flows collected during forty days throughout April, May, and June 2021.
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spelling pubmed-87492222022-01-13 Hornet 40: Network dataset of geographically placed honeypots Valeros, Veronica Garcia, Sebastian Data Brief Data Article Deception technologies, and honeypots in particular, have been used for decades to understand how cyber attacks and attackers work. A myriad of factors impact the effectiveness of a honeypot. However, very few is known about the impact of the geographical location of honeypots on the amount and type of attacks. Hornet 40 is the first dataset designed to help understand how the geolocation of honeypots may impact the inflow of network attacks. The data consists of network flows in binary and text format, with up to 118 features, including 480 bytes of the content of each flow. They were created using the Argus flow collector. The passive honeypots are IP addresses connected to the Internet and do not have any honeypot software running, so attacks are not interactive. The data was collected from identically configured honeypot servers in eight locations: Amsterdam, Bangalore, Frankfurt, London, New York, San Francisco, Singapore, and Toronto. The dataset contains over 4.7 million network flows collected during forty days throughout April, May, and June 2021. Elsevier 2022-01-05 /pmc/articles/PMC8749222/ /pubmed/35036492 http://dx.doi.org/10.1016/j.dib.2022.107795 Text en © 2022 The Authors. Published by Elsevier Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Data Article
Valeros, Veronica
Garcia, Sebastian
Hornet 40: Network dataset of geographically placed honeypots
title Hornet 40: Network dataset of geographically placed honeypots
title_full Hornet 40: Network dataset of geographically placed honeypots
title_fullStr Hornet 40: Network dataset of geographically placed honeypots
title_full_unstemmed Hornet 40: Network dataset of geographically placed honeypots
title_short Hornet 40: Network dataset of geographically placed honeypots
title_sort hornet 40: network dataset of geographically placed honeypots
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8749222/
https://www.ncbi.nlm.nih.gov/pubmed/35036492
http://dx.doi.org/10.1016/j.dib.2022.107795
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