Bahmanisangesari, Saeid2024-07-182024-07-182024-06https://unbscholar.lib.unb.ca/handle/1882/38052The timely prediction of Common Vulnerability Severity Scores (CVSS) following the release of Common Vulnerabilities and Exposures (CVE) announcements is crucial for enhancing cybersecurity responsiveness. A delay in acquiring these scores may make it more difficult to prioritize risks effectively, resulting in the misallocation of resources and a delay in mitigating actions. Long exposure to untreated vulnerabilities also raises the possibility of exploitative attacks, which could lead to serious breaches of security that compromise data integrity and harm users and organizations. This thesis develops a multi-step predictive model that leverages DistilBERT, a distilled version of the BERT architecture, and Artificial Neural Networks (ANNs) to predict CVSS scores prior to their official release. Utilizing a dataset from the National Vulnerability Database (NVD), the research examines the effectiveness of incorporating contextual information from CVE source identifiers and the benefits of incremental learning in improving model accuracy. The models achieved better results compared to the top-performing models among other works with an average accuracy of 91.96% in predicting CVSS category scores and an average F1 score of 91.87%. The results demonstrate the model’s capability to predict CVSS scores across multiple categories effectively, thereby potentially reducing the response time to cybersecurity threats.xiii, 111electronicenhttp://purl.org/coar/access_right/c_abf2master thesisGhorbani, Ali A.Isah, HarunaComputer Science