Enhancing Vulnerability Reports with Automated and Augmented Description Summarization
Hattan Althebeiti, Mohammed Alkinoon, Manar Mohaisen, Saeed Salem, DaeHun Nyang, David Mohaisen·April 29, 2025
Summary
Zad enhances National Vulnerability Database descriptions, using a fine-tuned summarization model to improve understanding and prioritization of cybersecurity vulnerabilities. It addresses dataset deficiencies, enhancing content relatedness and detail. Evaluated for effectiveness, Zad demonstrates capability in vulnerability information enhancement through computational and human metric assessments.
Introduction
Background
Overview of the National Vulnerability Database (NVD)
Importance of cybersecurity vulnerability descriptions
Objective
Aim of Zad: Improving understanding and prioritization of cybersecurity vulnerabilities
Method
Data Collection
Source of data: National Vulnerability Database (NVD)
Data Preprocessing
Handling dataset deficiencies
Enhancing content relatedness and detail
Model Fine-tuning
Utilization of a summarization model
Customization for cybersecurity vulnerability descriptions
Evaluation
Computational assessment methods
Human evaluation for effectiveness
Results
Improvement in Descriptions
Enhanced clarity and detail
Prioritization and Understanding
Improved prioritization of vulnerabilities
Enhanced understanding for cybersecurity professionals
Conclusion
Summary of Findings
Implications for Cybersecurity
Future Work
Potential advancements in summarization models
Integration with existing cybersecurity tools
Basic info
papers
cryptography and security
machine learning
artificial intelligence
Advanced features
Insights
How does the fine-tuned summarization model enhance the descriptions in the National Vulnerability Database?
What are the key implementation steps for Zad in improving cybersecurity vulnerability descriptions?
How does Zad address dataset deficiencies to enhance content relatedness and detail?