  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.
