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arXiv 2407.06759cs.CRcs.SE
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网络安全防御：通过攻击描述探究CVE类型

Cybersecurity Defenses: Exploration of CVE Types through Attack Descriptions

Refat Othman, Bruno Rossi, Barbara Russo

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AI总结：

针对软件漏洞难以及时发现的问题，提出基于MPNET句子变换器的分类工具VULDAT，用于从攻击描述识别系统漏洞，在对比实验中性能优于8种先进分类器，F1值达0.85。

摘要：

软件安全漏洞即便遭到利用后，仍可能未被发现。将攻击与漏洞关联起来，有助于专家及时识别并响应安全事件。本文提出了VULDAT，这是一款采用句子变换器MPNET的分类工具，可从攻击描述中识别系统漏洞。我们的模型在ATT&CK知识库的100种攻击技术以及CVE知识库的685个问题上进行了应用。随后，我们将VULDAT与其他8种基于句子变换器的先进分类器的性能进行了对比。结果表明，该模型取得了最优性能，F1值为0.85，精确率为0.86，召回率为0.83。此外，我们发现VULDAT识别出了56%与攻击相关的CVE报告漏洞，且61%的已识别漏洞存在于CVE知识库中。

英文摘要：

Vulnerabilities in software security can remain undiscovered even after being exploited. Linking attacks to vulnerabilities helps experts identify and respond promptly to the incident. This paper introduces VULDAT, a classification tool using a sentence transformer MPNET to identify system vulnerabilities from attack descriptions. Our model was applied to 100 attack techniques from the ATT&CK repository and 685 issues from the CVE repository. Then, we compare the performance of VULDAT against the other eight state-of-the-art classifiers based on sentence transformers. Our findings indicate that our model achieves the best performance with F1 score of 0.85, Precision of 0.86, and Recall of 0.83. Furthermore, we found 56% of CVE reports vulnerabilities associated with an attack were identified by VULDAT, and 61% of identified vulnerabilities were in the CVE repository.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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