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dc.contributor.authorMedenou Choumanof, Roumen Daton
dc.contributor.authorLlopis Sánchez, Salvador
dc.contributor.authorCalzado Mayo, Victor Manuel
dc.contributor.authorGarcia Balufo, Miriam
dc.contributor.authorPáramo Castrillo, Miguel
dc.contributor.authorGonzález Garrido, Francisco José
dc.contributor.authorLuis Martinez, Alvaro
dc.contributor.authorNevado Catalán, David
dc.contributor.authorHu, Ao
dc.contributor.authorRodriguez-Bermejo, David Sandoval
dc.contributor.authorPasqual De Riquelme, Gerardo Ramis
dc.contributor.authorSotelo Monge, Marco Antonio
dc.contributor.authorBerardi, Antonio
dc.contributor.authorDe Santis, Paolo
dc.contributor.authorTorelli, Francesco
dc.contributor.authorMaestre Vidal, Jorge
dc.date2022
dc.date.accessioned2023-02-22T13:51:30Z
dc.date.available2023-02-22T13:51:30Z
dc.identifier.citationMedenou Choumanof, R. D., Llopis Sanchez, S., Calzado Mayo, V. M., Garcia Balufo, M., Páramo Castrillo, M., González Garrido, F. J., ... & Maestre Vidal, J. (2022). Introducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awareness. Sensors, 22(14), 5104.es_ES
dc.identifier.issn1424-8220
dc.identifier.urihttps://reunir.unir.net/handle/123456789/14224
dc.description.abstractThe 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.es_ES
dc.language.isoenges_ES
dc.publisherSensorses_ES
dc.relation.ispartofseries;vol. 22, nº 14
dc.relation.urihttps://www.mdpi.com/1424-8220/22/14/5104es_ES
dc.rightsopenAccesses_ES
dc.subjectadvanced persistent threatses_ES
dc.subjectcyber defencees_ES
dc.subjectcyber situational awarenesses_ES
dc.subjectdatasetes_ES
dc.subjectdecision-makinges_ES
dc.subjectJCRes_ES
dc.subjectScopuses_ES
dc.titleIntroducing the CYSAS-S3 Dataset for Operationalizing a Mission-Oriented Cyber Situational Awarenesses_ES
dc.typeArticulo Revista Indexadaes_ES
reunir.tag~ARIes_ES
dc.identifier.doihttps://doi.org/10.3390/s22145104


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