Dynamic Industrial Cyber risk Modelling based on Evidence (DICYME)
X Jornadas Nacionales de Investigación en Ciberseguridad – JNIC 2025
Abstract
The increasing prevalence of cyber threats targeting industrial infrastructures highlights the urgent need for advanced cyber risk quantification models tailored to these environments. The DICYME project, led by DeNexus in collaboration with Rey Juan Carlos University (URJC), focuses on creating valuable datasets and cybersecurity risk indicators that enhance cyber risk quantification processes, whether within our proposed framework or other cyber risk quantification models.
Our contribution includes a cyber risk quantification approach that integrates curated datasets through automated data extraction, developing cybersecurity risk indicators, and statistical simulations, to enable dynamic data-driven risk assessments. These components are consolidated into a visualization tool, allowing industrial operators and insurers to analyze, quantify, and mitigate cyber risks with structured and evidence-based insights.
Citation
@inproceedings{fernández-isabel2025,
author = {Fernández-Isabel, Alberto and Martín de Diego, Isaac and
Cano, Emilio and Fernández, Rubén and García-Ochoa, Javier and R
Ravines, Romy and López, Ovidio and Puigbó, Jaume},
title = {Dynamic {Industrial} {Cyber} Risk {Modelling} Based on
{Evidence} {(DICYME)}},
booktitle = {X Jornadas Nacionales de Investigación en Ciberseguridad
– JNIC 2025},
volume = {1},
pages = {64-\/-67},
date = {2025-06-04},
url = {https://zaguan.unizar.es/record/161724/files/BOOK-2025-377.pdf?version=2},
isbn = {978-84-10169-61-6},
langid = {en}
}