Information Extraction of Cybersecurity Concepts: An LSTM Approach
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DISP Laboratory, Université Lumière Lyon 2, 69500 Lyon, France
2
Computer Science Department, College of Engineering, Qatar University, Doha P.O. Box 2713, Qatar;
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(19), 3945; https://doi.org/10.3390/app9193945
Received: 2 June 2019
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Revised: 14 July 2019
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Accepted: 17 July 2019
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Published: 20 September 2019
(This article belongs to the Special Issue Machine Learning for Cybersecurity Threats, Challenges, and Opportunities)
Extracting 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.
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Keywords:
cybersecurity text; information extraction; named entity recognition; relation extraction; NLP; recurrent neural networks; LSTM
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MDPI and ACS Style
Gasmi, H.; Laval, J.; Bouras, A. Information Extraction of Cybersecurity Concepts: An LSTM Approach. Appl. Sci. 2019, 9, 3945. https://doi.org/10.3390/app9193945
AMA Style
Gasmi H, Laval J, Bouras A. Information Extraction of Cybersecurity Concepts: An LSTM Approach. Applied Sciences. 2019; 9(19):3945. https://doi.org/10.3390/app9193945
Chicago/Turabian StyleGasmi, Houssem, Jannik Laval, and Abdelaziz Bouras. 2019. "Information Extraction of Cybersecurity Concepts: An LSTM Approach" Applied Sciences 9, no. 19: 3945. https://doi.org/10.3390/app9193945
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