Next Article in Journal
A Novel Modular Multiport Converter for Enhancing the Performance of Photovoltaic-Battery Based Power Systems
Next Article in Special Issue
Insider Threat Detection Based on User Behavior Modeling and Anomaly Detection Algorithms
Previous Article in Journal
Value-Oriented Requirements: Eliciting Domain Requirements from Social Network Services to Evolve Software Product Lines
Previous Article in Special Issue
Malware Detection Approach Based on Artifacts in Memory Image and Dynamic Analysis
Open AccessArticle

Information Extraction of Cybersecurity Concepts: An LSTM Approach

1
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 / Revised: 14 July 2019 / Accepted: 17 July 2019 / Published: 20 September 2019
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. View Full-Text
Keywords: cybersecurity text; information extraction; named entity recognition; relation extraction; NLP; recurrent neural networks; LSTM cybersecurity text; information extraction; named entity recognition; relation extraction; NLP; recurrent neural networks; LSTM
Show Figures

Figure 1

MDPI and ACS Style

Gasmi, H.; Laval, J.; Bouras, A. Information Extraction of Cybersecurity Concepts: An LSTM Approach. Appl. Sci. 2019, 9, 3945.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop