Security-related issues have become more significant due to the proliferation of IT. Collating security-related information in a database improves security. For example, Common Vulnerabilities and Exposures (CVE) is a security knowledge repository containing descriptions of vulnerabilities about software or source code. Although the descriptions include various entities, there is not a uniform entity structure, making security analysis difficult using individual entities. Developing a consistent entity structure will enhance the security field. Herein we propose a method to automatically label select entities from CVE descriptions by applying the Named Entity Recognition (NER) technique. We manually labeled 3287 CVE descriptions and conducted experiments using a machine learning model called BERT to compare the proposed method to labeling with regular expressions. Machine learning using the proposed method significantly improves the labeling accuracy. It has an f1 score of about 0.93, precision of about 0.91, and recall of about 0.95, demonstrating that our method has potential to automatically label select entities from CVE descriptions.

System administrators and security officials of an organization need to deal with vulnerable IT assets, especially those with severe vulnerabilities, to minimize the risk of these vulnerabilities being exploited. The Common Vulnerability Scoring System (CVSS) can be used as a means to calculate the severity score of vulnerabilities, but it currently requires human operators to choose input values. A word-level Convolutional Neural Network (CNN) has been proposed to estimate the input parameters of CVSS and derive the severity score of vulnerability notes, but its accuracy needs to be improved further. In this paper, we propose a character-level CNN for estimating the severity scores. Experiments show that the proposed scheme outperforms conventional one in terms of accuracy and how errors occur.