Workshop on Machine Learning for Cybersecurity, International Conference on Machine Learning

Recognizing and Extracting Cybersecurity Entities from Text

Casey Hanks, Michael Maiden, Priyanka Ranade, Tim Finin, and Anupam Joshi

July 17, 2022

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.

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Tags: cybersecurity, entity recognition, natural language processing, ner

Type: InProceedings

Publisher: PLMR

Downloads: 1422 downloads