Andrii Skrylnyk

Type of the article: Theoretical Article

Abstract
National cybersecurity opens new opportunities for digital transformation and helps counter the growing threat of cyberattacks. The purpose of this study is to develop a methodology and identify the most influential predictors of national cybersecurity in global competitiveness, utilizing the data mining approach. To assess the impact of cybersecurity characteristics, modern machine learning methods were implemented in the Salford Predictive Modeler software environment, including Classification and Regression Trees, Random Forest, Stochastic Gradient Boosting, Multivariate Adaptive Regression Splines, Generalized Path Seeker, and classical regression. Data mining was conducted using the World Competitiveness Ranking (WCR) indicators, constructed by the Institute for Management Development, and the National Cybersecurity Index (NCSI), constructed by the e-Governance Academy, for 64 countries as of 2024. Three groups of countries were identified with different predicted levels of competitiveness, underscoring the importance of balancing the development of both external and internal components of cyber defense. The empirical results demonstrate that countries with strong cybersecurity management capabilities achieve higher competitive positions, even with limited participation in global cyber initiatives. At the same time, active global involvement without sufficient internal readiness does not create sustainable competitive advantages. The regression tree provided a transparent allocation of key subfactors, with global cybersecurity contribution and cybersecurity management playing leading roles. The significance of the study lies in shaping methodology and state policy in cybersecurity, human resource development, regulatory framework enhancement, and digital ecosystem strengthening.

Acknowledgments
The paper is prepared within the scientific research projects “Digital transformations to ensure civil protection and post-war economic recovery in the face of environmental and social challenges” (№0124U000549) and “Drivers and barriers to human capital transformation for a circular and green economy” (No. 0126U001080).