# Recognizing and Extracting Cybersecurtity-relevant Entities from Text

arXiv: https://arxiv.org/abs/2208.01693 · HTML: https://mlsanity.com/paper/2208.01693/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 2 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Neural Architectures for Named Entity Recognition](https://mlsanity.com/paper/1603.01360/) (2016)
- [COVID-19 Knowledge Graph: Accelerating Information Retrieval and Discovery for Scientific Literature](https://mlsanity.com/paper/2007.12731/) (2020)
- [Automatic Labeling for Entity Extraction in Cyber Security](https://mlsanity.com/paper/1308.4941/) (2013)
- [RelExt: Relation Extraction using Deep Learning approaches for Cybersecurity Knowledge Graph Improvement](https://mlsanity.com/paper/1905.02497/) (2019)
- [Cyber-All-Intel: An AI for Security related Threat Intelligence](https://mlsanity.com/paper/1905.02895/) (2019)
- [CyNER: A Python Library for Cybersecurity Named Entity Recognition](https://mlsanity.com/paper/2204.05754/) (2022)

## Built on by (most cited first, 1 of 1)

- [Cream Skimming the Underground: Identifying Relevant Information Points from Online Forums](https://mlsanity.com/paper/2308.02581/) (2023)

## Abstract (arXiv)

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.