Back
Conference paper Open Access Scopus Source The journal / series is indexed in Scopus (SCImago list). The article itself has not been verified. 33 Citations

Methodology for predictive cyber security risk assessment (PCSRA)

Ferreira, D.J. , Mateus-Coelho, N. , Mamede, H.S.
ISLA — Instituto Politécnico de Gestão e Tecnologia
2023, pp. 1555–1563

Resume

Abstract
With the current impulse of Cyberattacks, data becomes of central importance. There are many challenges in how they are used that also need to be discussed. Defining the suitable cybersecurity incident response model is a critical challenge that all companies face today. With a high number of incidents that happen daily and for which there is not always an adequate response due to the lack of predictive models based on data (evidence), there is a significant investment in research to identify the main factors that can cause such incidents, in and consistently trying to have the most appropriate answer, and ultimately, boosting responsiveness and success. At the same time, there are several different methodologies to assess the risk management of organizations and their level of maturity. The capability assessment is intended to enable organizations to understand better the fundamentals that need to be laid down to deliver cybersecurity successfully. There is, however, a gap in determining an organization's degree of proactive responsiveness to successfully embracing cybersecurity and an even more significant gap in assessing it from a risk management perspective. This work proposes a model to assess this capacity.

Keywords

#incident response #risk management maturity #capability assessment #data-driven predictive models #Computer science #Computer security #Risk analysis (engineering) #Work (physics) #Risk management

Bibliographic Details

Publication Type Conference paper
Publication Year 2023
ISSN 18770509
Pagination pp. 1555–1563
Scopus Indexing Indexed source (via SCImago)

ISLA Authors & Affiliations

N
Nuno Ricardo Mateus Coelho
nuno.coelho@islagaia.pt ID: A514-DAF9-ECB2
Profile