Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness

[EN] The digital transformation of the defence sector is not exempt from innovative requirements and challenges, with the lack of availability of reliable, unbiased and consistent data for training automatisms (machine learning algorithms, decision-making, what-if recreation of operational condition...

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Detalles Bibliográficos
Autores: Medenou Choumanof, Roumen Daton, Llopis Sánchez, Salvador, Calzado Mayo, Victor Manuel, Garcia Balufo, Miriam, Páramo Castrillo, Miguel, González Garrido, Francisco José, Martinez, Álvaro, Nevado Catalán, David, Hu, Ao, Sandoval Rodríguez-Bermejo, David, Ramis Pasqual De Riquelme, Gerardo, Sotelo Monge, Marco Antonio, Berardi, Antonio, De Santis, Paolo, Torelli, Francesco, Maestre
Tipo de recurso: artículo
Fecha de publicación:2022
País:España
Institución:Universitat Politècnica de València (UPV)
Repositorio:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
Idioma:inglés
OAI Identifier:oai:riunet.upv.es:10251/202696
Acceso en línea:https://riunet.upv.es/handle/10251/202696
Access Level:acceso abierto
Palabra clave:Advanced persistent threats
Cyber defence
Cyber situational awareness
Dataset
Decision-making
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spelling Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational AwarenessMedenou Choumanof, Roumen DatonLlopis Sánchez, SalvadorCalzado Mayo, Victor ManuelGarcia Balufo, MiriamPáramo Castrillo, MiguelGonzález Garrido, Francisco JoséMartinez, ÁlvaroNevado Catalán, DavidHu, AoSandoval Rodríguez-Bermejo, DavidRamis Pasqual De Riquelme, GerardoSotelo Monge, Marco AntonioBerardi, AntonioDe Santis, PaoloTorelli, FrancescoMaestreAdvanced persistent threatsCyber defenceCyber situational awarenessDatasetDecision-making[EN] The digital transformation of the defence sector is not exempt from innovative requirements and challenges, with the lack of availability of reliable, unbiased and consistent data for training automatisms (machine learning algorithms, decision-making, what-if recreation of operational conditions, support the human understanding of the hybrid operational picture, personnel training/education, etc.) being one of the most relevant gaps. In the context of cyber defence, the state-of-the-art provides a plethora of data network collections that tend to lack presenting the information of all communication layers (physical to application). They are synthetically generated in scenarios far from the singularities of cyber defence operations. None of these data network collections took into consideration usage profiles and specific environments directly related to acquiring a cyber situational awareness, typically missing the relationship between incidents registered at the hardware/software level and their impact on the military mission assets and objectives, which consequently bypasses the entire chain of dependencies between strategic, operational, tactical and technical domains. In order to contribute to the mitigation of these gaps, this paper introduces CYSAS-S3, a novel dataset designed and created as a result of a joint research action that explores the principal needs for datasets by cyber defence centres, resulting in the generation of a collection of samples that correlate the impact of selected Advanced Persistent Threats (APT) with each phase of their cyber kill chain, regarding mission-level operations and goals.MDPI AGRepositorio Institucional de la Universitat Politècnica de València Riunet20222022-07-01journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://riunet.upv.es/handle/10251/202696reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valénciainstname:Universitat Politècnica de València (UPV)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Reconocimiento (by)http://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:riunet.upv.es:10251/2026962026-06-13T07:49:27Z
dc.title.none.fl_str_mv Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
title Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
spellingShingle Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
Medenou Choumanof, Roumen Daton
Advanced persistent threats
Cyber defence
Cyber situational awareness
Dataset
Decision-making
title_short Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
title_full Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
title_fullStr Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
title_full_unstemmed Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
title_sort Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness
dc.creator.none.fl_str_mv Medenou Choumanof, Roumen Daton
Llopis Sánchez, Salvador
Calzado Mayo, Victor Manuel
Garcia Balufo, Miriam
Páramo Castrillo, Miguel
González Garrido, Francisco José
Martinez, Álvaro
Nevado Catalán, David
Hu, Ao
Sandoval Rodríguez-Bermejo, David
Ramis Pasqual De Riquelme, Gerardo
Sotelo Monge, Marco Antonio
Berardi, Antonio
De Santis, Paolo
Torelli, Francesco
Maestre
author Medenou Choumanof, Roumen Daton
author_facet Medenou Choumanof, Roumen Daton
Llopis Sánchez, Salvador
Calzado Mayo, Victor Manuel
Garcia Balufo, Miriam
Páramo Castrillo, Miguel
González Garrido, Francisco José
Martinez, Álvaro
Nevado Catalán, David
Hu, Ao
Sandoval Rodríguez-Bermejo, David
Ramis Pasqual De Riquelme, Gerardo
Sotelo Monge, Marco Antonio
Berardi, Antonio
De Santis, Paolo
Torelli, Francesco
Maestre
author_role author
author2 Llopis Sánchez, Salvador
Calzado Mayo, Victor Manuel
Garcia Balufo, Miriam
Páramo Castrillo, Miguel
González Garrido, Francisco José
Martinez, Álvaro
Nevado Catalán, David
Hu, Ao
Sandoval Rodríguez-Bermejo, David
Ramis Pasqual De Riquelme, Gerardo
Sotelo Monge, Marco Antonio
Berardi, Antonio
De Santis, Paolo
Torelli, Francesco
Maestre
author2_role author
author
author
author
author
author
author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Repositorio Institucional de la Universitat Politècnica de València Riunet
dc.subject.none.fl_str_mv Advanced persistent threats
Cyber defence
Cyber situational awareness
Dataset
Decision-making
topic Advanced persistent threats
Cyber defence
Cyber situational awareness
Dataset
Decision-making
description [EN] The digital transformation of the defence sector is not exempt from innovative requirements and challenges, with the lack of availability of reliable, unbiased and consistent data for training automatisms (machine learning algorithms, decision-making, what-if recreation of operational conditions, support the human understanding of the hybrid operational picture, personnel training/education, etc.) being one of the most relevant gaps. In the context of cyber defence, the state-of-the-art provides a plethora of data network collections that tend to lack presenting the information of all communication layers (physical to application). They are synthetically generated in scenarios far from the singularities of cyber defence operations. None of these data network collections took into consideration usage profiles and specific environments directly related to acquiring a cyber situational awareness, typically missing the relationship between incidents registered at the hardware/software level and their impact on the military mission assets and objectives, which consequently bypasses the entire chain of dependencies between strategic, operational, tactical and technical domains. In order to contribute to the mitigation of these gaps, this paper introduces CYSAS-S3, a novel dataset designed and created as a result of a joint research action that explores the principal needs for datasets by cyber defence centres, resulting in the generation of a collection of samples that correlate the impact of selected Advanced Persistent Threats (APT) with each phase of their cyber kill chain, regarding mission-level operations and goals.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022-07-01
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://riunet.upv.es/handle/10251/202696
url https://riunet.upv.es/handle/10251/202696
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Reconocimiento (by)
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI AG
publisher.none.fl_str_mv MDPI AG
dc.source.none.fl_str_mv reponame:RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname:Universitat Politècnica de València (UPV)
instname_str Universitat Politècnica de València (UPV)
reponame_str RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
collection RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869418590284808192
score 15,301603

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