TY - GEN
T1 - Graph-Based Profiling of Dependency Vulnerability Remediation
AU - Buschmann, Fernando Vera
AU - Pauliuchenka, Palina
AU - Oh, Ethan
AU - Kao, Bai Chien
AU - DiValentin, Louis
AU - Bader, David A.
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - This research presents an enhanced Graph Attention Convolutional Neural Network (GAT) tailored for the analysis of open-source package vulnerability remediation. By meticulously examining control flow graphs and implementing node centrality metrics-specifically, degree, norm, and closeness centrality-our methodology identifies and evaluates changes resulting from vulnerability fixes in nodes, thereby predicting the ramifications of dependency upgrades on application workflows. Empirical testing on diverse datasets reveals that our model challenges established paradigms in software security, showcasing its efficacy in delivering comprehensive insights into code vulnerabilities and contributing to advancements in cybersecurity practices. This study delineates a strategic framework for the development of sustainable monitoring systems and the effective remediation of vulnerabilities in open-source software.
AB - This research presents an enhanced Graph Attention Convolutional Neural Network (GAT) tailored for the analysis of open-source package vulnerability remediation. By meticulously examining control flow graphs and implementing node centrality metrics-specifically, degree, norm, and closeness centrality-our methodology identifies and evaluates changes resulting from vulnerability fixes in nodes, thereby predicting the ramifications of dependency upgrades on application workflows. Empirical testing on diverse datasets reveals that our model challenges established paradigms in software security, showcasing its efficacy in delivering comprehensive insights into code vulnerabilities and contributing to advancements in cybersecurity practices. This study delineates a strategic framework for the development of sustainable monitoring systems and the effective remediation of vulnerabilities in open-source software.
KW - Code Vulnerability Mitigation
KW - Cybersecurity
KW - Deep Learning Applications
KW - Graph Attention Convolutional Neural Network (GAT)
KW - Knowledge Graph
KW - Network Analysis
KW - Node Centrality Metrics
KW - open source
KW - package upgrade
KW - Package Vulnerability Analysis
UR - https://www.scopus.com/pages/publications/105000772358
UR - https://www.scopus.com/inward/citedby.url?scp=105000772358&partnerID=8YFLogxK
U2 - 10.1007/978-981-96-2417-1_8
DO - 10.1007/978-981-96-2417-1_8
M3 - Conference contribution
AN - SCOPUS:105000772358
SN - 9789819624164
T3 - Lecture Notes in Computer Science
SP - 138
EP - 157
BT - Science of Cyber Security - 6th International Conference, SciSec 2024, Proceedings
A2 - Zhao, Jun
A2 - Meng, Weizhi
PB - Springer Science and Business Media Deutschland GmbH
T2 - 6th International Conference on Science of Cyber Security, SciSec 2024
Y2 - 14 August 2024 through 16 August 2024
ER -