PREDICTIVE MODEL OF CYBER THREATS INTEGRATING CVE MITRE AND WAZUH THROUGH TEMPORAL MACHINE LEARNING - Marcos Borges Paes, Tadeu; Danilo Mendonça, Eudes (Journal of Interdisciplinary Debates, 2025)
The increasing complexity and accelerated volume of vulnerabilities recorded by the CVE MITRE reinforce the need for models capable of anticipating trends and supporting strategic decisions in information security. This article proposes a predictive model based on Temporal Machine Learning, integrating historical data from CVE MITRE with operational telemetry from Wazuh, in order to predict the evolution of vulnerability categories, severity levels, and attack vectors. The study expands the architecture already used in practical implementations of the Wazuh dashboard, incorporating advanced time series techniques, such as ARIMA, Prophet, and LSTM, to detect patterns and project future behaviors. The results demonstrate the potential of temporal analysis to reduce response times, optimize risk management, and increase organizational maturity in cybersecurity. The proposed approach represents a significant advance by transforming a traditionally reactive process into a predictive analytical system, integrable into corporate ecosystems for incident monitoring and response.