Cyber Threat Intelligence (CTI) is information describing threat vectors,
vulnerabilities, and attacks and is often used as training data for AI-based
cyber defense systems such as Cybersecurity Knowledge Graphs (CKG). There is a
strong need to develop community-accessible datasets to train existing AI-based
cybersecurity pipelines to efficiently and accurately extract meaningful
insights from CTI. We have created an initial unstructured CTI corpus from a
variety of open sources that we are using to train and test cybersecurity
entity models using the spaCy framework and exploring self-learning methods to
automatically recognize cybersecurity entities. We also describe methods to
apply cybersecurity domain entity linking with existing world knowledge from
Wikidata. Our future work will survey and test spaCy NLP tools and create
methods for continuous integration of new information extracted from text.

建立 CTI 数据集，使用 spaCy 框架和自我学习方法训练和测试网络安全实体模型，并探索将其与 Wikidata 的现有世界知识相链接的方法。

从文本中识别和提取与网络安全相关的实体

Recognizing and Extracting Cybersecurtity-relevant Entities from Text

Despite impressive success of machine learning algorithms in clinical natural
language processing (cNLP), rule-based approaches still have a prominent role.
In this paper, we introduce medspaCy, an extensible, open-source cNLP library
based on spaCy framework that allows flexible integration of rule-based and
machine learning-based algorithms adapted to clinical text. MedspaCy includes a
variety of components that meet common cNLP needs such as context analysis and
mapping to standard terminologies. By utilizing spaCy's clear and easy-to-use
conventions, medspaCy enables development of custom pipelines that integrate
easily with other spaCy-based modules. Our toolkit includes several core
components and facilitates rapid development of pipelines for clinical text.

介绍了 medspaCy，这是一个基于 spaCy 框架的可扩展的、开源的临床自然语言处理（cNLP）库，它允许灵活地集成基于规则和基于机器学习的算法来适应临床文本，包括多种元素，可以满足常见的 cNLP 需求，如上下文分析和映射到标准术语。