Next-Generation Security Entity Linkage: Harnessing the Power of Knowledge Graphs and Large Language

Daniel Alfasi, T. Shapira, A. Bremler-Barr
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Abstract

With the continuous increase in reported Common Vulnerabilities and Exposures (CVEs), security teams are overwhelmed by vast amounts of data, which are often analyzed manually, leading to a slow and inefficient process. To address cybersecurity threats effectively, it is essential to establish connections across multiple security entity databases, including CVEs, Common Weakness Enumeration (CWEs), and Common Attack Pattern Enumeration and Classification (CAPECs). In this study, we introduce a new approach that leverages the RotatE [4] knowledge graph embedding model, initialized with embeddings from Ada language model developed by OpenAI [3]. Additionally, we extend this approach by initializing the embeddings for the relations.
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下一代安全实体链接:利用知识图谱和大型语言的力量
随着报告的常见漏洞和暴露(cve)的不断增加,安全团队被大量数据所淹没,这些数据通常是手动分析的,导致流程缓慢且效率低下。为了有效地解决网络安全威胁,必须跨多个安全实体数据库建立连接,包括cve、通用弱点枚举(CWEs)和通用攻击模式枚举和分类(CAPECs)。在本研究中,我们引入了一种利用RotatE[4]知识图嵌入模型的新方法,该模型使用OpenAI[3]开发的Ada语言模型的嵌入进行初始化。此外,我们通过初始化关系的嵌入来扩展这种方法。
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