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Botnet dataset with simultaneous attack activity

The proposed dataset shows characteristics of simultaneous botnet attack activities. Botnet network traffic has sequentially interconnected as formed as bidirectional network flow (binetflow), which is combined with normal activities. The dataset is generated from a simulation process by extracting...

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Detalles Bibliográficos
Autores principales: Putra, Muhammad Aidiel Rachman, Hostiadi, Dandy Pramana, Ahmad, Tohari
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679537/
https://www.ncbi.nlm.nih.gov/pubmed/36426071
http://dx.doi.org/10.1016/j.dib.2022.108628
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author Putra, Muhammad Aidiel Rachman
Hostiadi, Dandy Pramana
Ahmad, Tohari
author_facet Putra, Muhammad Aidiel Rachman
Hostiadi, Dandy Pramana
Ahmad, Tohari
author_sort Putra, Muhammad Aidiel Rachman
collection PubMed
description The proposed dataset shows characteristics of simultaneous botnet attack activities. Botnet network traffic has sequentially interconnected as formed as bidirectional network flow (binetflow), which is combined with normal activities. The dataset is generated from a simulation process by extracting botnet pattern behaviors taken from CTU-13 and NCC datasets. The extraction results are utilized as the basis for simulations to produce a new dataset with simultaneous botnet attack activities. The term “simultaneous attack activities” refers to an attack activity that involves multiple botnets and happens at the same time. The dataset contains several botnet types distributed over three detection sensors. Each dataset has 18 network header features with a total recording duration of 8 h. The bot attack spreads must be appropriately handled by efficient processing, also known as parallel computation detection.
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spelling pubmed-96795372022-11-23 Botnet dataset with simultaneous attack activity Putra, Muhammad Aidiel Rachman Hostiadi, Dandy Pramana Ahmad, Tohari Data Brief Data Article The proposed dataset shows characteristics of simultaneous botnet attack activities. Botnet network traffic has sequentially interconnected as formed as bidirectional network flow (binetflow), which is combined with normal activities. The dataset is generated from a simulation process by extracting botnet pattern behaviors taken from CTU-13 and NCC datasets. The extraction results are utilized as the basis for simulations to produce a new dataset with simultaneous botnet attack activities. The term “simultaneous attack activities” refers to an attack activity that involves multiple botnets and happens at the same time. The dataset contains several botnet types distributed over three detection sensors. Each dataset has 18 network header features with a total recording duration of 8 h. The bot attack spreads must be appropriately handled by efficient processing, also known as parallel computation detection. Elsevier 2022-09-24 /pmc/articles/PMC9679537/ /pubmed/36426071 http://dx.doi.org/10.1016/j.dib.2022.108628 Text en © 2022 The Author(s). Published by Elsevier Inc. https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Putra, Muhammad Aidiel Rachman
Hostiadi, Dandy Pramana
Ahmad, Tohari
Botnet dataset with simultaneous attack activity
title Botnet dataset with simultaneous attack activity
title_full Botnet dataset with simultaneous attack activity
title_fullStr Botnet dataset with simultaneous attack activity
title_full_unstemmed Botnet dataset with simultaneous attack activity
title_short Botnet dataset with simultaneous attack activity
title_sort botnet dataset with simultaneous attack activity
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9679537/
https://www.ncbi.nlm.nih.gov/pubmed/36426071
http://dx.doi.org/10.1016/j.dib.2022.108628
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