VCGERG: Vulnerability Classification With Graph Embedding Algorithm on Vulnerability Report Graphs

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Author(s): Yashu Liu (Beijing University of Civil Engineering and Architecture, China), Xiaoyi Zhao (Beijing University of Civil Engineering and Architecture, China), Xiaohua Qiu (Beijing University of Civil Engineering and Architecture, China)and Han-Bing Yan (National Computer Network Emergency Response Technical Team, China)
Copyright: 2024
Volume: 18
Issue: 1
Pages: 21
Source title: International Journal of Information Security and Privacy (IJISP)
Editor(s)-in-Chief: Yassine Maleh (Sultan Moulay Slimane University, Morocco)and Ahmed A. Abd El-Latif (Menoufia University, Egypt)
DOI: 10.4018/IJISP.342596

Keywords: Information Science Reference / Information Security & Privacy / IT Security and Ethics / Security & Forensics

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Abstract

Vulnerability can lead to data loss, privacy leakage and financial loss. Accurate detection and identification of vulnerabilities is essential to prevent information leakage and APT attacks. This paper explores the possibility of digging the valuable information in vulnerability reports deeply. We propose a new model, VCGERG, which products a graph using key information from vulnerability reports and embeds the graph into the vector space using a keywords-LINE graph embedding algorithm based on the attention of neighboring nodes. VCGERG model uses the OVR random forest algorithm to classify vulnerabilities. Our model can get the complicated local and global information of the graph in large-scale dataset and achieve better results. In order to verify the effectiveness of our model, it is evaluated on many experiments. Compared with other models, our method has a higher accuracy rate of 0.975.

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VCGERG: Vulnerability Classification With Graph Embedding Algorithm on Vulnerability Report Graphs

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