Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents
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 adjust...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Symmetry |
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| Online Access: | https://www.mdpi.com/2073-8994/12/3/354 |
| _version_ | 1798005486551826432 |
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| author | Tiberiu-Marian Georgescu |
| author_facet | Tiberiu-Marian Georgescu |
| author_sort | Tiberiu-Marian Georgescu |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T12:41:09Z |
| format | Article |
| id | doaj.art-9cad5db014a2487f874550122067db4d |
| institution | Directory Open Access Journal |
| issn | 2073-8994 |
| language | English |
| last_indexed | 2024-04-11T12:41:09Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Symmetry |
| spelling | doaj.art-9cad5db014a2487f874550122067db4d2022-12-22T04:23:30ZengMDPI AGSymmetry2073-89942020-03-0112335410.3390/sym12030354sym12030354Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related DocumentsTiberiu-Marian Georgescu0Department of Economic Informatics and Cybernetics, The Bucharest University of Economic Studies, 6 Piata Romana, 010374 Bucharest, RomaniaThis 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.https://www.mdpi.com/2073-8994/12/3/354cybersecuritymachine learningontologiesnamed entity recognitionnatural language processingrelation extraction |
| spellingShingle | Tiberiu-Marian Georgescu Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents Symmetry cybersecurity machine learning ontologies named entity recognition natural language processing relation extraction |
| title | Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents |
| title_full | Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents |
| title_fullStr | Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents |
| title_full_unstemmed | Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents |
| title_short | Natural Language Processing Model for Automatic Analysis of Cybersecurity-Related Documents |
| title_sort | natural language processing model for automatic analysis of cybersecurity related documents |
| topic | cybersecurity machine learning ontologies named entity recognition natural language processing relation extraction |
| url | https://www.mdpi.com/2073-8994/12/3/354 |
| work_keys_str_mv | AT tiberiumariangeorgescu naturallanguageprocessingmodelforautomaticanalysisofcybersecurityrelateddocuments |