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FixV2W uses knowledge graph embeddings to improve CVE-CWE mapping accuracy

By PulseAugur Editorial · [2 sources] ·

Researchers have developed FixV2W, a novel method to enhance the accuracy of mappings between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries. This approach utilizes knowledge graph embeddings and historical data analysis to correct inconsistencies found in public databases like the National Vulnerability Database (NVD). The system demonstrated significant improvements, correctly mapping 69% of exploited vulnerabilities with prior invalid CWEs and boosting the Mean Reciprocal Rank (MRR) for machine learning models from 0.174 to 0.608. AI

IMPACT Improves accuracy of vulnerability data used by ML models, potentially aiding threat detection.

RANK_REASON Academic paper detailing a new methodology for improving vulnerability mapping.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

FixV2W uses knowledge graph embeddings to improve CVE-CWE mapping accuracy

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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Sevval Simsek, Varsha Athreya, David Starobinski ·

    FixV2W: Correcting Invalid CVE-CWE Mappings with Knowledge Graph Embeddings

    arXiv:2604.22176v1 Announce Type: cross Abstract: Accurate mapping between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries is critical for effective vulnerability management and risk assessment. However, public databases, such as the Natio…

  2. arXiv cs.LG TIER_1 English(EN) · David Starobinski ·

    FixV2W: Correcting Invalid CVE-CWE Mappings with Knowledge Graph Embeddings

    Accurate mapping between Common Vulnerabilities and Exposures (CVE) and Common Weakness Enumeration (CWE) entries is critical for effective vulnerability management and risk assessment. However, public databases, such as the National Vulnerability Database (NVD), suffer from inco…

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