Open Cyber threat intelligence (OpenCTI) information is available in an unstructured format from heterogeneous sources on the Internet. We present CyNER, an open-source python library for cybersecurity named entity recognition (NER). CyNER combines transformer-based models for extracting cybersecurity-related entities, heuristics for extracting different indicators of compromise, and publicly available NER models for generic entity types. We provide models trained on a diverse corpus that users can readily use. Events are described as classes in previous research - MALOnt2.0 (Christian et al., 2021) and MALOnt (Rastogi et al., 2020) and together extract a wide range of malware attack details from a threat intelligence corpus. The user can combine predictions from multiple different approaches to suit their needs. The library is made publicly available.
@article{arxiv.2204.05754,
title = {CyNER: A Python Library for Cybersecurity Named Entity Recognition},
author = {Md Tanvirul Alam and Dipkamal Bhusal and Youngja Park and Nidhi Rastogi},
journal= {arXiv preprint arXiv:2204.05754},
year = {2022}
}