A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for Cybersecurity Article Swipe
Soham Dasgupta
,
Aritran Piplai
,
Anantaa Kotal
,
Anupam Joshi
·
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/bigdata50022.2020.9378482
YOU?
·
· 2020
· Open Access
·
· DOI: https://doi.org/10.1109/bigdata50022.2020.9378482
Named Entity Recognition (NER) is important in the cybersecurity domain. It helps researchers extract cyber threat information from unstructured text sources. The extracted cyber entities or key expressions can be used to model a cyber-attack described in an open-source text. A large number of general-purpose NER algorithms have been published that work well in text analysis. These algorithms do not perform well when applied to the cybersecurity domain. In the field of cybersecurity, the open-source text available varies greatly in complexity and underlying structure of the sentences. General-purpose NER algorithms can misrepresent domain-specific words, such as “malicious” and “javascript”. In this paper, we compare the recent deep learning-based NER algorithms on a cybersecurity dataset. We created a cybersecurity dataset collected from various sources, including “Microsoft Security Bulletin” and “Adobe Security Updates”. Some of these approaches proposed in the literature were not used for cybersecurity. Others are innovations proposed by us. This comparative study helps us identify the NER algorithms that are robust and can work well in sentences taken from a large number of cybersecurity sources. We tabulate their performance on the test set and identify the best NER algorithm for a cybersecurity corpus. We also discuss the different embedding strategies that aid in the process of NER for the chosen deep learning algorithms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/bigdata50022.2020.9378482
- OA Status
- green
- Cited By
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W3111854523Canonical identifier for this work in OpenAlex
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https://doi.org/10.1109/bigdata50022.2020.9378482Digital Object Identifier
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A Comparative Study of Deep Learning based Named Entity Recognition Algorithms for CybersecurityWork title
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2020Year of publication
- Publication date
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2020-12-10Full publication date if available
- Authors
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Soham Dasgupta, Aritran Piplai, Anantaa Kotal, Anupam JoshiList of authors in order
- Landing page
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https://doi.org/10.1109/bigdata50022.2020.9378482Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.13016/m2wds7-b7n8Direct OA link when available
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Computer science, Named-entity recognition, Domain (mathematical analysis), Artificial intelligence, JavaScript, Algorithm, Computer security, Machine learning, Information retrieval, World Wide Web, Task (project management), Mathematics, Economics, Management, Mathematical analysisTop concepts (fields/topics) attached by OpenAlex
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29Total citation count in OpenAlex
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2025: 5, 2024: 8, 2023: 7, 2022: 4, 2021: 5Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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