LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866910606576058368 |
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| author | Gasmi, Houssem Laval, Jannik Bouras, Abdelaziz |
| author_facet | Gasmi, Houssem Laval, Jannik Bouras, Abdelaziz |
| contents | The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities, such as product, version, attack name, etc. in technical documents. Although generally considered a simple task in the information extraction field, it is quite challenging in some domains like cybersecurity because of the complex structure of its entities. The state of the art methods require time-consuming and labor intensive feature engineering that describes the properties of the entities, their context, domain knowledge, and linguistic characteristics. The model demonstrated in this paper is domain independent and does not rely on any features specific to the entities in the cybersecurity domain, hence does not require expert knowledge to perform feature engineering. The method used relies on a type of recurrent neural networks called Long Short-Term Memory (LSTM) and the Conditional Random Fields (CRFs) method. The results we obtained showed that this method outperforms the state of the art methods given an annotated corpus of a decent size. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2409_10521 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition Gasmi, Houssem Laval, Jannik Bouras, Abdelaziz Information Retrieval Artificial Intelligence Cryptography and Security Machine Learning The automated and timely conversion of cybersecurity information from unstructured online sources, such as blogs and articles to more formal representations has become a necessity for many applications in the domain nowadays. Named Entity Recognition (NER) is one of the early phases towards this goal. It involves the detection of the relevant domain entities, such as product, version, attack name, etc. in technical documents. Although generally considered a simple task in the information extraction field, it is quite challenging in some domains like cybersecurity because of the complex structure of its entities. The state of the art methods require time-consuming and labor intensive feature engineering that describes the properties of the entities, their context, domain knowledge, and linguistic characteristics. The model demonstrated in this paper is domain independent and does not rely on any features specific to the entities in the cybersecurity domain, hence does not require expert knowledge to perform feature engineering. The method used relies on a type of recurrent neural networks called Long Short-Term Memory (LSTM) and the Conditional Random Fields (CRFs) method. The results we obtained showed that this method outperforms the state of the art methods given an annotated corpus of a decent size. |
| title | LSTM Recurrent Neural Networks for Cybersecurity Named Entity Recognition |
| topic | Information Retrieval Artificial Intelligence Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2409.10521 |