Automated Severity Driven Patch Management - Banjar, Carlos Eduardo de Schuller; Bicudo, Miguel Angelo Santos; Miranda, Lucas; Pereira, Cainã Figueiredo; Coutinho, Lucas Senos; Menasche, Daniel Sadoc; Srivastava, Gaurav Kumar; Lovat, Enrico; Kocheturov, Anton; Martins, Matheus; Aguiar, Leandro Pfleger de (Anais Estendidos do Latin-American Symposium on Dependable and Secure Computing (LADC), 2024)
We present a method for assessing the temporal severity associated with software vulnerabilities by analyzing reported vulnerability data. Data from various platforms is collected and curated to define specific vulnerability features and historical vulnerability event data. When a vulnerability is specified, the system identifies its vulnerability class using a classifier based on the predefined features. Historical event data is then processed to generate a predictive severity curve, which estimates the evolution of a temporal severity score, parameterized by the occurrence of key vulnerability events. This curve predicts the time of weaponization and/or exploitation events, along with the corresponding severity score for the specified vulnerability. Our approach aims to support automated decision-making in software patch management by enabling accurate tracking and prediction of vulnerability severity over time.