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.

本研究针对当前实体对齐技术的局限性，通过标注第一个网络安全领域实体对齐数据集，并揭示了安全实体的独特特征，提出了第一个网络安全实体对齐模型，CEAM，具有不对称遮蔽聚合和分区关注两种机制，实验结果表明CEAM在网络安全领域的实体对齐数据集上显著优于同类别实体对齐方法。

网络安全实体对齐：基于掩码图注意力网络的方法