Year: 2022
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Qin, Yue, Liao, Xiaojing
Institution: Indiana University Bloomington
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4283832093