Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: CyNER: A Python Library for Cybersecurity Named Entity Recognition   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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The categories of this paper are: cond-mat
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The categories of this paper are: hep-ph
24
The categories of this paper are: dg-ga
21
The categories of this paper are: adap-org
31
The categories of this paper are: ao-sci
9
The categories of this paper are: nlin
20
The categories of this paper are: acc-phys
7
The categories of this paper are: hep-th
29
The categories of this paper are: funct-an
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The categories of this paper are: nucl-ex
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The categories of this paper are: patt-sol
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The categories of this paper are: q-fin
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The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex