An Approach of Security Model to Mitigate Risk of Cyberattacks on Public Institutions in Ecuador

Segundo Moisés T. Toapanta, Peter S. Muñoz Campuzano, Dario Fernando Huilcapi, Luis Enrique Mafla Gallegos

Producción científica: Capítulo del libro/informe/acta de congresoContribución de conferenciarevisión exhaustiva

Resumen

Security problems due to cyberattacks were analyzed, checked security models designed to protect and preserve databases of information from these institutions, using all the parameters of information security. It is planted propose a prototype of a security model to mitigate the risks of cyberattacks on public institutions to define the organizational structure and security levels in order to adopt security protocols. A security model was proposed, and a general network topology that helped meet the objective. The deductive method was used in the investigation to verify the current trends of security models and to protect the information available on the ministerial entity. The prototype of a security model was established to mitigate the risks of cyberattacks. It is concluded that a security strategy are necessary data through a correct, reliable and highly available route that offers a higher level of reliability. The content of this plan was presented and made available to all members of the public institution, especially those who are actively involved in safety procedures of information.

Idioma originalInglés
Título de la publicación alojadaProceedings of the 2020 4th International Conference on Information System and Data Mining, ICISDM 2020
EditorialAssociation for Computing Machinery
Páginas51-57
Número de páginas7
ISBN (versión digital)9781450377652
DOI
EstadoPublicada - 15 may. 2020
Evento4th International Conference on Information System and Data Mining, ICISDM 2020 - Hilo, Estados Unidos
Duración: 15 may. 202017 may. 2020

Serie de la publicación

NombreACM International Conference Proceeding Series

Conferencia

Conferencia4th International Conference on Information System and Data Mining, ICISDM 2020
País/TerritorioEstados Unidos
CiudadHilo
Período15/05/2017/05/20

Nota bibliográfica

Publisher Copyright:
© 2020 ACM.

Copyright:
Copyright 2020 Elsevier B.V., All rights reserved.

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