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ROSIDS23: Network intrusion detection dataset for robot operating system

The data described herein pertains to the Robotic Systems Security domain. This data in brief presents the attributes of the ROSIDS23 dataset and its collection process in detail. This dataset comprises Robot Operating System (ROS)-based cyber-attacks to address the emerging need in high fidelity da...

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Autores principales: Değirmenci, Elif, Sabri Kırca, Yunus, Özçelik, İlker, Yazıcı, Ahmet
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654526/
https://www.ncbi.nlm.nih.gov/pubmed/38020425
http://dx.doi.org/10.1016/j.dib.2023.109739
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author Değirmenci, Elif
Sabri Kırca, Yunus
Özçelik, İlker
Yazıcı, Ahmet
author_facet Değirmenci, Elif
Sabri Kırca, Yunus
Özçelik, İlker
Yazıcı, Ahmet
author_sort Değirmenci, Elif
collection PubMed
description The data described herein pertains to the Robotic Systems Security domain. This data in brief presents the attributes of the ROSIDS23 dataset and its collection process in detail. This dataset comprises Robot Operating System (ROS)-based cyber-attacks to address the emerging need in high fidelity data for robotic system security research. The data was gathered from the IFARLab-DIH environment. IFARLab-DIH is a robotic and factory-level laboratory that includes a ROS-based network and is used to conduct studies up to TRL 5 on robotic systems. ROSIDS23 dataset contains benign and various attack traffic collected from the ROS middleware using the tcpdump network protocol analyser. Then the eighty-two traffic features were extracted from the captured pcap files and converted into CSV format using the CICFlowMeter tool. This dataset can serve as a valuable resource for developing and improving security countermeasures in robotic systems and can help the evolution of resilient robotics infrastructure.
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spelling pubmed-106545262023-10-31 ROSIDS23: Network intrusion detection dataset for robot operating system Değirmenci, Elif Sabri Kırca, Yunus Özçelik, İlker Yazıcı, Ahmet Data Brief Data Article The data described herein pertains to the Robotic Systems Security domain. This data in brief presents the attributes of the ROSIDS23 dataset and its collection process in detail. This dataset comprises Robot Operating System (ROS)-based cyber-attacks to address the emerging need in high fidelity data for robotic system security research. The data was gathered from the IFARLab-DIH environment. IFARLab-DIH is a robotic and factory-level laboratory that includes a ROS-based network and is used to conduct studies up to TRL 5 on robotic systems. ROSIDS23 dataset contains benign and various attack traffic collected from the ROS middleware using the tcpdump network protocol analyser. Then the eighty-two traffic features were extracted from the captured pcap files and converted into CSV format using the CICFlowMeter tool. This dataset can serve as a valuable resource for developing and improving security countermeasures in robotic systems and can help the evolution of resilient robotics infrastructure. Elsevier 2023-10-31 /pmc/articles/PMC10654526/ /pubmed/38020425 http://dx.doi.org/10.1016/j.dib.2023.109739 Text en © 2023 The Author(s) 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
Değirmenci, Elif
Sabri Kırca, Yunus
Özçelik, İlker
Yazıcı, Ahmet
ROSIDS23: Network intrusion detection dataset for robot operating system
title ROSIDS23: Network intrusion detection dataset for robot operating system
title_full ROSIDS23: Network intrusion detection dataset for robot operating system
title_fullStr ROSIDS23: Network intrusion detection dataset for robot operating system
title_full_unstemmed ROSIDS23: Network intrusion detection dataset for robot operating system
title_short ROSIDS23: Network intrusion detection dataset for robot operating system
title_sort rosids23: network intrusion detection dataset for robot operating system
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10654526/
https://www.ncbi.nlm.nih.gov/pubmed/38020425
http://dx.doi.org/10.1016/j.dib.2023.109739
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