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English(EN) Athena: Vulnerability-Affected Library Identification via Knowledge Graph Completion

Athena系统利用知识图谱识别易受攻击的软件库

研究人员开发了Athena,一个新颖的基于图的系统,用于识别受软件漏洞影响的库。与将漏洞数据视为孤立文本的先前方法不同,Athena将此信息建模为知识图谱。该系统利用知识图谱补全技术来预测给定漏洞的缺失受影响库的详细信息。最终的重新排序模块通过结合图嵌入和LLM分析,进一步优化了这些预测,显著优于现有的最先进方法。 AI

影响 这项研究可以提高漏洞数据库的准确性,从而带来更安全的软件开发实践。

排序理由 该集群描述了一篇研究论文,其中详细介绍了一个用于特定技术问题的新系统和方法论。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

Athena系统利用知识图谱识别易受攻击的软件库

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该集群描述了一篇研究论文,其中详细介绍了一个用于特定技术问题的新系统和方法论。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Phong Trinh Duy, Trang Dang Yen, Hung Nguyen-Huu, Bach Le, Quyet-Thang Huynh, Dieu Hoang Vu, David Lo, Thanh Le-Cong ·

    Athena:通过知识图谱补全进行易受攻击库的识别

    arXiv:2609.01187v1 Announce Type: cross Abstract: A single vulnerability in a widely used library can cascade through millions of dependent applications, yet more than half of vulnerability database entries contain missing or incorrect affected-library information. Existing autom…