Skip navigation
  •  Inicio
  • UDC 
    • Cómo depositar
    • Políticas del RUC
    • FAQ
    • Derechos de autor
    • Más información en INFOguías UDC
  • Listar 
    • Comunidades
    • Buscar por:
    • Fecha de publicación
    • Autor
    • Título
    • Materia
  • Ayuda
    • español
    • Gallegan
    • English
  • Acceder
  •  Español 
    • Español
    • Galego
    • English
  
Ver ítem 
  •   RUC
  • Facultade de Informática
  • Investigación (FIC)
  • Ver ítem
  •   RUC
  • Facultade de Informática
  • Investigación (FIC)
  • Ver ítem
JavaScript is disabled for your browser. Some features of this site may not work without it.

Time Aware F-Score for Cybersecurity Early Detection Evaluation

Thumbnail
Ver/Abrir
Lopez_Vizcaino_Manuel_2024_Time_Aware_F_Score_for_Cybersecurity_Early_Detection_Evaluation.pdf (546.8Kb)
Use este enlace para citar
http://hdl.handle.net/2183/36648
Colecciones
  • Investigación (FIC) [1685]
Metadatos
Mostrar el registro completo del ítem
Título
Time Aware F-Score for Cybersecurity Early Detection Evaluation
Autor(es)
López-Vizcaíno, Manuel F.
Novoa, Francisco
Fernández, Diego
Cacheda, Fidel
Fecha
2024-01
Cita bibliográfica
López-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/app14020574
Resumen
[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.
Palabras clave
Early detection
Machine learning
Classification algorithms
Network security
Social networks
Time-aware metrics
 
Versión del editor
https://doi.org/10.3390/app14020574
Derechos
Atribución 4.0 Internacional
ISSN
2076-3417

Listar

Todo RUCComunidades & ColeccionesPor fecha de publicaciónAutoresTítulosMateriasGrupo de InvestigaciónTitulaciónEsta colecciónPor fecha de publicaciónAutoresTítulosMateriasGrupo de InvestigaciónTitulación

Mi cuenta

AccederRegistro

Estadísticas

Ver Estadísticas de uso
Sherpa
OpenArchives
OAIster
Scholar Google
UNIVERSIDADE DA CORUÑA. Servizo de Biblioteca.    DSpace Software Copyright © 2002-2013 Duraspace - Sugerencias