Cybersecurity Policy Classification, National Clustering, and Transnational Transfer Based on SVM and HCA

Abstract

This paper proposes a cybersecurity policy analysis and transfer model integrating natural language processing (NLP), support vector machines (SVM), hierarchical cluster analysis (HCA), and network analysis. It focuses on exploring the patterns of utility flow in policy classification, country clustering, and policy transfer. First, a dataset is constructed by extracting policy text word vectors using TF-IDF. SVM classifies policies into four categories: prevention, accountability, cooperation, and emergency response. Policy effectiveness is calculated by comparing crime counts between control and experimental groups. Second, using per capita GDP, higher education enrollment rate, and internet penetration rate as indicators, countries undergo data standardization. Euclidean distance measures clustering distance, and HCA categorizes 141 countries into five groups. Finally, a multiple linear regression model linking countries and cybercrime was constructed. The policy transfer guidance coefficient (incorporating non-transfer scenarios and the weakest-link effect) was defined. Combined with cosine similarity calculations for country similarity, relative policy effect coefficients were derived. Network analysis visualized policy efficacy diffusion. This model enables precise policy classification, scientific country clustering, and efficient policy transfer. Its advantage lies in integrating multiple algorithms to enhance the scientific rigor of analysis and policy transfer.