  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.
