Work

Enhancing Vulnerability Reports With Automated and Augmented Description Summarization

Year: 2025

Type: article

Abstract: Public vulnerability databases, such as the National Vulnerability Database (NVD), document vulnerabilities and facilitate threat information sharing. However, they often suffer from short descriptions and outdated or insufficient information. In this paper, we introduceZad, a system designed to enrich NVD vulnerability descriptions by leveraging external resources.Zadconsists of two pipelines: one collects and filters supplementary data using two encoders to build a detailed dataset, while the other fine-tunes a pre-trained model on this dataset to generate enriched descriptions. By addressing brevity and improving content quality,Zadproduces more comprehensive and cohesive vulnerability descriptions. We evaluateZadusing standard summarization metrics and human assessments, demonstrating its effectiveness in enhancing vulnerability information.

Source: IEEE Transactions on Big Data

DOI: 10.1109/tbdata.2025.3566618

Authors: Hattan Althebeiti, Mohammed Alkinoon, Manar Mohaisen, Saeed Salem, DaeHun Nyang, David Mohaisen

Institutions: University of Central Florida, Northeastern Illinois University, Qatar University, Ewha Womans University

Language: English

Topic: Topic Modeling

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: green

Funders: National Research Foundation, Taif University

Award: G1101055935