EMNLP 2022
Conference paper

Full-Stack Information Extraction System for Cybersecurity Intelligence


Due to rapidly growing cyber-attacks and security vulnerabilities, many reports on cyber-threat intelligence (CTI) are being published daily. While these reports can help security analysts to understand ongoing cyber threats, the overwhelming amount of information makes it difficult to digest the information in a timely manner. This paper presents, SecIE, an industrial-strength full-stack information extraction (IE) system for the security domain. SecIE can extract a large number of security entities, relations and the temporal information of the relations, which is critical for cyberthreat investigations. Our evaluation with 133 labeled threat reports containing 108,021 tokens shows that SecIE achieves over 92% F1-score for entity extraction and about 70% F1-score for relation extraction. We also showcase how SecIE can be used for downstream security applications.