Cybersecurity Diagnosis Based on National Context

Abstract

With rapid internet advancement, strengthening cybersecurity is vital for national security. This study uses a Five-Dimensional K-means Clustering (FDKC), a Partition-based Spearman Correlation Analysis, and a TREND-enhanced AHP-EWM model to analyze cybercrime patterns and guide policy. First, five indicators—Crime Rate, Success Rate, Report Rate, Thwart Rate, and Prosecution Rate—were used in FDKC to group countries into five clusters. Results show that technologically advanced countries like the US and UK have the highest Crime Rates, while regions with weaker cybersecurity, such as Russia, the Middle East, and Africa, have higher Success Rates. Countries with strong cybersecurity frameworks, including the US, China, and Europe, show higher Report, Thwart, and Prosecution Rates. Second, integrating the Global Cybersecurity Index (GCI) and policy timelines, the Spearman model revealed that policy effectiveness depends on economic and technological context: less developed nations gain most from international cooperation, while developed countries benefit from stronger legislation and technical measures. Third, twelve demographic variables across Population Structure, Internet Access, Wealth, and Education were analyzed with the AHP-EWM model incorporating trend factors, confirming that moderately developed countries should focus on education to improve cybersecurity awareness and reduce cybercrime. Finally, sensitivity analysis confirmed the model’s robustness and suitability for policy evaluation and strategic cybersecurity planning.