.png){kind=icon}
Return
Vulnerability2Vec: A Graph-Embedding Approach for Enhancing Vulnerability Classification
.png){kind=icon}
OA
AI
DOI：10.32604/cmes.2025.068723.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
The escalating complexity and heterogeneity of modern energy systems-particularly in smart grid and distributed energy infrastructures-has intensified the need for intelligent and scalable security vulnerability classification. To address this challenge, we propose Vulnerability2Vec, a graph-embedding-based framework designed to enhance the automated classification of security vulnerabilities that threaten energy system resilience. Vulnerability2Vec converts Common Vulnerabilities and Exposures (CVE) text explanations to semantic graphs, where nodes represent CVE IDs and key terms (nouns, verbs, and adjectives), and edges capture co-occurrence relationships. Then, it embeds the semantic graphs to a low-dimensional vector space with random-walk sampling and skip-gram with negative sampling. It is possible to identify the latent relationships and structural patterns that traditional sparse vector methods fail to capture. Experimental results demonstrate a classification accuracy of up to 80%, significantly outperforming baseline methods. This approach offers a theoretical basis for classifying vulnerability types as structured semantic patterns in complex software systems. The proposed method models the semantic structure of vulnerabilities, providing a theoretical foundation for their classification.
Keywords:
Security vulnerability
graph representation
graph-embedding
deep learning
node classification
Journal
C
IF:
2.5
Papers:
44
Citations:
4.4K
Organization
No organization information available
Cited Papers
Network visualization and analysis of gene expression data using BioLayout Express3D.png)
NATURE PROTOCOLS
NATURE PROTOCOLSIF16
PREAI
ShareSaveShareSaveShareSaveWord Equations: Inherently Interpretable Sparse Word Embeddings through Sparse CodingProceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLPOAAI
Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLPIF0
OAAIShareSaveShareSaveShareSave
ShareSave
View more