Profile of Vulnerability Remediations in Dependencies Using Graph Analysis
CoRR(2024)
Abstract
This research introduces graph analysis methods and a modified Graph
Attention Convolutional Neural Network (GAT) to the critical challenge of open
source package vulnerability remediation by analyzing control flow graphs to
profile breaking changes in applications occurring from dependency upgrades
intended to remediate vulnerabilities. Our approach uniquely applies node
centrality metrics – degree, norm, and closeness centrality – to the GAT
model, enabling a detailed examination of package code interactions with a
focus on identifying and understanding vulnerable nodes, and when dependency
package upgrades will interfere with application workflow. The study's
application on a varied dataset reveals an unexpected limited
inter-connectivity of vulnerabilities in core code, thus challenging
established notions in software security. The results demonstrate the
effectiveness of the enhanced GAT model in offering nuanced insights into the
relational dynamics of code vulnerabilities, proving its potential in advancing
cybersecurity measures. This approach not only aids in the strategic mitigation
of vulnerabilities but also lays the groundwork for the development of
sophisticated, sustainable monitoring systems for the evaluation of work effort
for vulnerability remediation resulting from open source software. The insights
gained from this study mark a significant advancement in the field of package
vulnerability analysis and cybersecurity.
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