Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents
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This paper describes the development and implementation of a natural language processing model based on machine learning which performs cognitive analysis for cybersecurity-related documents. A domain ontology was developed using a two-step approach: (1) the symmetry stage and (2) the machine adjustment. The first stage is based on the symmetry between the way humans represent a domain and the way machine learning solutions do. Therefore, the cybersecurity field was initially modeled based on the expertise of cybersecurity professionals. A dictionary of relevant entities was created; the entities were classified into 29 categories and later implemented as classes in a natural language processing model based on machine learning. After running successive performance tests, the ontology was remodeled from 29 to 18 classes. Using the ontology, a natural language processing model based on a supervised learning model was defined. We trained the model using sets of approximately 300,000 words. Remarkably, our model obtained an F1 score of 0.81 for named entity recognition and 0.58 for relation extraction, showing superior results compared to other similar models identified in the literature. Furthermore, in order to be easily used and tested, a web application that integrates our model as the core component was developed.
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Georgescu et al. (2020) used an NLP model with a domain ontology and outperformed competing models with F1 scores of 0.58 for relation extraction and 0.81 for NER.
“Georgescu et al. (2020) [50]used anNLP model to cognitively analyze cybersecurity-related materials based on a domain ontology and outperformed competing models with F1 scores of 0.58 for relation extraction and 0.81 for NER”
From A systematic review on insider threat detection using natural language processing (2025) · cites this paper as [50]
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References 17 · Cited by 55
Research works printed but not linked: 3
- Bridges, R.A.; Jones, C.L.; Iannacone, M.D.; Testa, K.M.; Goodall, J.R. Automatic labeling for entity extraction. arXiv 2013, arXiv:1308.4941. Available online: https://arxiv.org/abs/1308.4941 (accessed on 8 February 2020). · arXiv
- Ferrucci, D.; Brown, E.; Chu-Carroll, J.; Fan, J.; Gondek, D.; Kalyanpur, A.A.; Lally, A.; Murdock, J.W.; Nyberg, E.; Prager, J.; et al. The AI Magazine 110. 2010. Available online: http://www.aaai.org/Magazine/ Watson/watson.php (accessed on 20 September 2019).
- Georgescu, T.M. Modelarea Bazată pe Volume Mari de Date. Securitate Cibernetică în Contextul Big Data (Eng. "Modelling Based on Large Volumes of Data. Cybersecurity in the Context of Big Data"). Ph.D. Thesis, The Bucharest University of Economic Studies, Bucharest, Romania, 2019.
- Authors
- Georgescu, T.-M.
- Affiliations
- Bucharest University of Economic Studies (RO)
- Corresponding author
- Georgescu, Tiberiu-Marian
- Venue
- Symmetry
- Publisher
- MDPI AG
- Published
- 2020-03-02
- Volume
- 12
- Issue
- 3
- Pages
- 354
- ISSN
- 2073-8994 (ISSN-L)
- OpenAlex
- W3009683167
- Cited by
- 55
- References
- 17
- Citations by year
- 2026: 6 · 2025: 12 · 2024: 21 · 2023: 5 · 2022: 7 · 2021: 4
- FWCI
- 1.57
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Symmetry (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- UEFISCDI through the PN III –"Dezvoltarea sistemului national de cercetare-dezvoltare" (PN-III-P1-1.2-PCCDI: 2017-0272)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Biomedical Text Mining and Ontologies
- Keywords
- domain ontology, named entity recognition, relation extraction, cybersecurity terminology, entity classification, supervised learning models, web application
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