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

Cybersecurity Entity Alignment via Masked Graph Attention Networks

Disinformation and fake news have posed detrimental effects on individuals
and society in recent years, attracting broad attention to fake news detection.
The majority of existing fake news detection algorithms focus on mining news
content and/or the surrounding exogenous context for discovering deceptive
signals; while the endogenous preference of a user when he/she decides to
spread a piece of fake news or not is ignored. The confirmation bias theory has
indicated that a user is more likely to spread a piece of fake news when it
confirms his/her existing beliefs/preferences. Users' historical, social
engagements such as posts provide rich information about users' preferences
toward news and have great potential to advance fake news detection. However,
the work on exploring user preference for fake news detection is somewhat
limited. Therefore, in this paper, we study the novel problem of exploiting
user preference for fake news detection. We propose a new framework, UPFD,
which simultaneously captures various signals from user preferences by joint
content and graph modeling. Experimental results on real-world datasets
demonstrate the effectiveness of the proposed framework. We release our code
and data as a benchmark for GNN-based fake news detection:
this https URL

本论文探讨了利用用户偏好来检测假新闻的问题，提出了一种同时捕获用户喜好信号的新框架 UPFD，应用图建模来检测假新闻并在真实数据集上取得了实验效果。