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dc.contributor.authorLópez-Vizcaíno, Manuel F.
dc.contributor.authorNovoa, Francisco
dc.contributor.authorFernández, Diego
dc.contributor.authorCacheda, Fidel
dc.date.accessioned2024-05-27T15:06:52Z
dc.date.available2024-05-27T15:06:52Z
dc.date.issued2024-01
dc.identifier.citationLópez-Vizcaíno, M.; Nóvoa, F.J.; Fernández, D.; Cacheda, F. Time Aware F-Score for Cybersecurity Early Detection Evaluation. Appl. Sci. 2024, 14(2), 574. https://doi.org/10.3390/app14020574es_ES
dc.identifier.issn2076-3417
dc.identifier.urihttp://hdl.handle.net/2183/36648
dc.description.abstract[Abstract]: With the increase in the use of Internet interconnected systems, security has become of utmost importance. One key element to guarantee an adequate level of security is being able to detect the threat as soon as possible, decreasing the risk of consequences derived from those actions. In this paper, a new metric for early detection system evaluation that takes into account the delay in detection is defined. Time aware F-score (TaF) takes into account the number of items or individual elements processed to determine if an element is an anomaly or if it is not relevant to be detected. These results are validated by means of a dual approach to cybersecurity, Operative System (OS) scan attack as part of systems and network security and the detection of depression in social media networks as part of the protection of users. Also, different approaches, oriented towards studying the impact of single item selection, are applied to final decisions. This study allows to establish that nitems selection method is usually the best option for early detection systems. TaF metric provides, as well, an adequate alternative for time sensitive detection evaluation.es_ES
dc.description.sponsorshipThis work was supported in part by the Ministry of Economy and Competitiveness of Spain and Fondo Europeo de Desarrollo Regional (FEDER) Funds of the European Union under Project PID2019-111388GB-I00; and in part by the Centro de Investigación de Galicia—Centro de Investigación en Tecnologías de la Información y las Comunicaciones (CITIC) Funded by Xunta de Galicia and the European Union (European Regional Development Fund–Galicia 2014-2020 Program), under Grant ED431G 2019/01.es_ES
dc.description.sponsorshipXunta de Galicia; ED431G 2019/01es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-111388GB-I00/ES/DETECCION TEMPRANA DE INTRUSIONES Y ANOMALIAS EN REDES DEFINIDAS POR SOFTWAREes_ES
dc.relation.urihttps://doi.org/10.3390/app14020574es_ES
dc.rightsAtribución 4.0 Internacionales_ES
dc.subjectEarly detectiones_ES
dc.subjectMachine learninges_ES
dc.subjectClassification algorithmses_ES
dc.subjectNetwork securityes_ES
dc.subjectSocial networkses_ES
dc.subjectTime-aware metricses_ES
dc.titleTime Aware F-Score for Cybersecurity Early Detection Evaluationes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.rights.accessinfo:eu-repo/semantics/openAccesses_ES
UDC.journalTitleApplied Scienceses_ES
UDC.volume14es_ES
UDC.issue2es_ES
UDC.startPage1es_ES
UDC.endPage12es_ES
dc.identifier.doi10.3390/app14020574


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