CASIE: Extracting Cybersecurity Event Information from Text

Authors

  • Taneeya Satyapanich University of Maryland, Baltimore County
  • Francis Ferraro University of Maryland, Baltimore County
  • Tim Finin University of Maryland, Baltimore County

DOI:

https://doi.org/10.1609/aaai.v34i05.6401

Abstract

We present CASIE, a system that extracts information about cybersecurity events from text and populates a semantic model, with the ultimate goal of integration into a knowledge graph of cybersecurity data. It was trained on a new corpus of 1,000 English news articles from 2017–2019 that are labeled with rich, event-based annotations and that covers both cyberattack and vulnerability-related events. Our model defines five event subtypes along with their semantic roles and 20 event-relevant argument types (e.g., file, device, software, money). CASIE uses different deep neural networks approaches with attention and can incorporate rich linguistic features and word embeddings. We have conducted experiments on each component in the event detection pipeline and the results show that each subsystem performs well.

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Published

2020-04-03

How to Cite

Satyapanich, T., Ferraro, F., & Finin, T. (2020). CASIE: Extracting Cybersecurity Event Information from Text. Proceedings of the AAAI Conference on Artificial Intelligence, 34(05), 8749-8757. https://doi.org/10.1609/aaai.v34i05.6401

Issue

Section

AAAI Technical Track: Natural Language Processing