CASIE: Extracting Cybersecurity Event Information from Text

Author/Creator ORCID





Citation of Original Publication

Satyapanich, Taneeya; Ferraro, Francis; Finin, Tim; CASIE: Extracting Cybersecurity Event Information from Text; 34th AAAI Confference on Artificial Intelligence, New York, Feb. 2020;


This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.



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.