论文标题
通过蒙版图形注意网络对齐网络安全实体对齐
Cybersecurity Entity Alignment via Masked Graph Attention Networks
论文作者
论文摘要
网络安全漏洞信息通常由多个渠道记录，包括政府漏洞存储库，个人维护的漏洞收集平台或漏洞披露的电子邮件列表和论坛。来自不同渠道的漏洞信息可以使全面的威胁评估和快速部署到各种安全机制。但是，自动收集此类信息的努力受到当今实体一致性技术的局限性的阻碍。在我们的研究中，我们注释了第一个网络安全域实体对齐数据集并揭示安全实体的独特特征。基于这些观察结果，我们提出了第一个网络安全实体比对模型CEAM，该模型CAM，该模型为基于GNN的实体比对以两种机制：不对称的遮罩聚集和分区的注意力。网络安全域实体比对数据集的实验结果表明，CEAM显着超过了最先进的实体比对方法。
Cybersecurity vulnerability information is often recorded by multiple channels, including government vulnerability repositories, individual-maintained vulnerability-gathering platforms, or vulnerability-disclosure email lists and forums. Integrating vulnerability information from different channels enables comprehensive threat assessment and quick deployment to various security mechanisms. Efforts to automatically gather such information, however, are impeded by the limitations of today's entity alignment techniques. In our study, we annotate the first cybersecurity-domain entity alignment dataset and reveal the unique characteristics of security entities. Based on these observations, we propose the first cybersecurity entity alignment model, CEAM, which equips GNN-based entity alignment with two mechanisms: asymmetric masked aggregation and partitioned attention. Experimental results on cybersecurity-domain entity alignment datasets demonstrate that CEAM significantly outperforms state-of-the-art entity alignment methods.
