Estimating Country-Level Cybercrime Legal Framework Upgrades Through Markov State Classification of Regulatory Maturity

Cybercrime poses a growing transnational threat, prompting countries to develop regulatory frameworks aimed at strengthening legal enforcement and institutional capacity. However, substantial disparities persist in the maturity of national cybercrime legal systems. This study proposes a predictive modeling framework to estimate country-level cybercrime regulatory transitions using a combined Markov state-transition and supervised machine learning approach. Regulatory maturity scores for 194 countries across 2020 and 2024 are discretized into three states—Low, Medium, and High—and modeled as multi-class transition outcomes. An empirical Markov transition matrix is first constructed to estimate baseline probabilistic movement between regulatory states. Results indicate strong path dependence, with low-maturity countries exhibiting high persistence and medium-level systems demonstrating greater volatility. High-level regulatory systems show partial stability but also evidence of boundary-sensitive transitions. To enhance predictive capability, a Random Forest classifier is trained using baseline maturity score, income level, and regional classification as structural predictors. Cross-validated performance achieves moderate predictive accuracy, suggesting that regulatory transitions are partially but not fully predictable from structural variables. Feature importance analysis reveals that baseline regulatory maturity is the dominant determinant of subsequent state transitions, while income and regional factors provide limited incremental explanatory power. The discrepancy between in-sample fit and cross-validation performance highlights the importance of reproducible evaluation and cautious interpretation of training metrics. The findings suggest that cybercrime legal framework development is primarily driven by institutional inertia and cumulative capacity, rather than rapid structural transformation. By integrating probabilistic transition modeling with machine learning classification, this study advances a governance-oriented perspective on cybersecurity analytics. The proposed framework offers a reproducible foundation for forecasting regulatory upgrades and informing international capacity-building strategies.