Name
Abstract | ||
|---|---|---|
Information extraction is an important semantic processing task to construct network security knowledge graph. Extracting entities and relationships in vulnerability description from public data sets will inevitably lead to waste of manpower and difficulty in accurate positioning. Another challenge is that there are multiple relationships among vulnerable descriptors. This paper proposes a framework for the common vulnerabilities and exposures (CVE) analysis, which consists of entity annotation algorithm and relational classification model. In particular, we apply the model to CVE dataset to solve the problem of information extraction and relationship classification in the CVE vulnerability analysis. Moreover, the predicted relationship is used to construct vulnerability security knowledge graph. The experimental results show that the framework can deal with the CVE vulnerability description effectively, and has good relationship classification performance. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/CBD54617.2021.00026 | 2021 Ninth International Conference on Advanced Cloud and Big Data (CBD) |
Keywords | DocType | ISBN |
relation classification,vulnerability security,CVE,information extraction,knowledge graph | Conference | 978-1-6654-0746-5 |
Citations | PageRank | References |
0 | 0.34 | 8 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhiyu Liu | 1 | 16 | 10.55 |
| XiaoQiang Di | 2 | 0 | 0.34 |
| Wei Song | 3 | 256 | 44.41 |
| WeiWu Ren | 4 | 0 | 0.34 |