Abstract:
"Abstract
Vulnerability management is a critical aspect of cybersecurity, requiring organizations to swiftly identify, assess, and address risks within their systems. However, the overwhelming volume and technical complexity of vulnerability reports present challenges to timely and effective decision-making. This paper introduces an Intelligent Vulnerability Report Parser (IVRP), an advanced solution designed to streamline the analysis of vulnerability data through automation and artificial intelligence (AI). The IVRP employs natural language processing (NLP) techniques to extract, standardize, and prioritize key information from diverse vulnerability reports, including those published by vendors, open-source communities, and threat intelligence platforms.
The parser is capable of recognizing and contextualizing critical data points such as Common Vulnerabilities and Exposures (CVE) identifiers, severity scores, affected systems, and mitigation recommendations. It leverages machine learning algorithms to assess the relevance and urgency of vulnerabilities in specific organizational contexts, enabling targeted remediation efforts. Additionally, the system integrates seamlessly with existing vulnerability management tools, fostering a more cohesive and efficient workflow.
This study evaluates the effectiveness of the IVRP using real-world datasets and benchmarks its performance against traditional manual analysis methods. Results demonstrate a significant reduction in processing time and human error while improving the accuracy and relevance of extracted insights. Furthermore, the IVRP’s adaptive learning capabilities ensure its continual improvement in parsing accuracy and contextual understanding as new vulnerabilities and reporting formats emerge.
By automating the labor-intensive task of parsing and prioritizing vulnerability data, the IVRP empowers organizations to proactively address security risks with precision and agility. This innovative approach enhances cybersecurity resilience while optimizing resource allocation, making it an indispensable tool for modern vulnerability management programs."
