ALGORITHM FOR MULTI-FACTORED FORECASTING OF NETWORK VULNERABILITIES: FROM CVE DATA ANALYSIS TO THE DEVELOPMENT OF A FORECASTING MODEL

This paper presents an algorithm for multi-factored forecasting of network vulnerabilities based on data analysis from the CVE (Common Vulnerabilities and Exposures) database. The goal of the research is to develop and implement a model capable of not only detecting existing vulnerabilities but also predicting their future occurrence using formal methods and machine learning. The authors conduct an overview and comparative analysis of modern forecasting methods, including statistical approaches, machine learning algorithms (specifically Random Forest), clustering, and artificial intelligence methods. As part of the study, a forecasting program was developed, which uses data on vulnerability types, severity levels (CVSS), software, and the year of detection. The model, based on the Random Forest algorithm, demonstrated high forecasting accuracy (94.14%). The results were visualized in the form of graphs and charts, reflecting the dynamics and distribution of vulnerabilities by year and software categories. The results of the work confirm the effectiveness of the proposed approach and demonstrate its potential application in cybersecurity systems for early threat detection. In the future, it is planned to integrate more complex models, account for new factors, and create a system for automatic threat notifications.