Abstract: Vulnerabilities in software systems are inevitable, but proper mitigation strategies can greatly reduce the risk to organizations. The Common Vulnerabilities and Exposures (CVE) list makes vulnerability information readily available and organizations rely on this information to effectively mitigate vulnerabilities in their systems. CVEs are classified into Common Weakness Enumeration (CWE) categories based on their underlying weaknesses and semantics. This classification provides an understanding of software flaws, their potential impacts, and means to detect, fix and prevent them. This understanding can help security administrators efficiently allocate resources to address critical security issues. However, mapping of CVEs to CWEs is mostly a manual process. To address this limitation, we introduce CVE2CWE, an automated approach for mapping Common Vulnerabilities and Exposures (CVEs) to Common Weakness Enumeration (CWE) entries. Leveraging natural language processing techniques, CVE
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