The Common Vulnerabilities and Exposures (CVE) are pivotal information for
proactive cybersecurity measures, including service patching, security
hardening, and more. However, CVEs typically offer low-level, product-oriented
descriptions of publicly disclosed cybersecurity vulnerabilities, often lacking
the essential attack semantic information required for comprehensive weakness
characterization and threat impact estimation. This critical insight is
essential for CVE prioritization and the identification of potential
countermeasures, particularly when dealing with a large number of CVEs. Current
industry practices involve manual evaluation of CVEs to assess their attack
severities using the Common Vulnerability Scoring System (CVSS) and mapping
them to Common Weakness Enumeration (CWE) for potential mitigation
identification. Unfortunately, this manual analysis presents a major bottleneck
in the vulnerability analysis process, leading to slowdowns in proactive
cybersecurity efforts and the potential for inaccuracies due to human errors.
In this research, we introduce our novel predictive model and tool (called
CVEDrill) which revolutionizes CVE analysis and threat prioritization. CVEDrill
accurately estimates the CVSS vector for precise threat mitigation and priority
ranking and seamlessly automates the classification of CVEs into the
appropriate CWE hierarchy classes. By harnessing CVEDrill, organizations can
now implement cybersecurity countermeasure mitigation with unparalleled
accuracy and timeliness, surpassing in this domain the capabilities of
state-of-the-art tools like ChaptGPT.

本研究介绍了一种名为 CVEDrill 的创新预测模型与工具，能够革新 CVE 分析和威胁优先级排序，实现精确威胁缓解和自动分类 CVE 至适当的 CWE 层级类别，从而开创了在此领域超越 ChaptGPT 等最先进工具的组织实施网络安全对策缓解的精确度和及时性。