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New research predicts multi-vulnerability attack chains using SBOM graphs

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的方法，通过利用软件物料清单（SBOM）数据来预测软件供应链中的多漏洞攻击链。该方法将SBOM视为依赖约束证据图，整合了组件和漏洞信息。训练了一个异构图注意力网络（HGAT）来对与漏洞相关的组件进行分类，准确率达到91.03%，F1分数达到74.02%。此外，还使用多层感知器（MLP）模型来预测级联漏洞，在已记录的攻击链上展示了0.93的ROC-AUC。 AI

影响 引入了一种新颖的图学习方法，用于预测复杂的软件供应链攻击。

排序理由 学术论文，详细介绍了一种预测软件漏洞的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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New research predicts multi-vulnerability attack chains using SBOM graphs

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学术论文，详细介绍了一种预测软件漏洞的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Laura Baird, Armin Moin ·

    从软件物料清单图谱预测软件供应链中的多漏洞攻击链

    arXiv:2604.04977v2 Announce Type: replace-cross Abstract: Software supply chain security compromises often stem from cascaded interactions of vulnerabilities, for example, between multiple vulnerable components. Yet, Software Bill of Materials (SBOM)-based pipelines for security …

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