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AuthorGasmi H.
AuthorLaval J.
AuthorBouras A.
Available date2020-04-09T07:35:03Z
Publication Date2019
Publication NameApplied Sciences (Switzerland)
ResourceScopus
ISSN20763417
URIhttp://dx.doi.org/10.3390/app9193945
URIhttp://hdl.handle.net/10576/13961
AbstractExtracting cybersecurity entities and the relationships between them from online textual resources such as articles, bulletins, and blogs and converting these resources into more structured and formal representations has important applications in cybersecurity research and is valuable for professional practitioners. Previous works to accomplish this task were mainly based on utilizing feature-based models. Feature-based models are time-consuming and need labor-intensive feature engineering to describe the properties of entities, domain knowledge, entity context, and linguistic characteristics. Therefore, to alleviate the need for feature engineering, we propose the usage of neural network models, specifically the long short-term memory (LSTM) models to accomplish the tasks of Named Entity Recognition (NER) and Relation Extraction (RE).We evaluated the proposed models on two tasks. The first task is performing NER and evaluating the results against the state-of-the-art Conditional Random Fields (CRFs) method. The second task is performing RE using three LSTM models and comparing their results to assess which model is more suitable for the domain of cybersecurity. The proposed models achieved competitive performance with less feature-engineering work. We demonstrate that exploiting neural network models in cybersecurity text mining is effective and practical. - 2019 by the authors.
SponsorThis publication was made possible by the support of Qatar University and DISP laboratory (Lumi?re University Lyon 2, France).
Languageen
PublisherMDPI AG
SubjectCybersecurity text
Information extraction
LSTM
Named entity recognition
NLP
Recurrent neural networks
Relation extraction
TitleInformation extraction of cybersecurity concepts: An LSTM approach
TypeArticle
Issue Number19
Volume Number9


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