CyNER: A Python Library for Cybersecurity Named Entity Recognition

• URL: http://arxiv.org/abs/2204.05754v1
• Date: Fri, 8 Apr 2022 16:49:32 GMT
• Title: CyNER: A Python Library for Cybersecurity Named Entity Recognition
• Authors: Md Tanvirul Alam, Dipkamal Bhusal, Youngja Park, Nidhi Rastogi
• Abstract summary: CyNER is an open-source python library for cybersecurity entity recognition. We provide models trained on a diverse corpus that users can readily use. The library is made publicly available.
• Score: 3.871148938060281
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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