Automated CPE Labeling of CVE Summaries with Machine Learning

Abstract

Open Source Security and Dependency Vulnerability Management (DVM) has become a more vital part of the software security stack in recent years as modern software tend to be more dependent on open source libraries. The largest open source of vulnerabilities is the National Vulnerability Database (NVD), which supplies developers with machine-readable vulnerabilities. However, sometimes Common Vulnerabilities and Exposures (CVE) have not been labeled with a Common Platform Enumeration (CPE) -version, -product and -vendor. This makes it very hard to automatically discover these vulnerabilities from import statements in dependency files. We, therefore, propose an automatic process of matching CVE summaries with CPEs through the machine learning task called Named Entity Recognition (NER). Our proposed model achieves an F-measure of 0.86 with a precision of 0.857 and a recall of 0.865, outperforming previous research for automated CPE-labeling of CVEs.