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Open AccessArticle

Text Mining in Cybersecurity: Exploring Threats and Opportunities

Data Science, TNO, 2592 DA The Hague, The Netherlands
Strategy and Policy, TNO, 2592 DA The Hague, The Netherlands
BloomingData, 2512 XA The Hague, The Netherlands
Leiden University Centre for Linguistics (LUCL), Leiden University, 2311 BX Leiden, The Netherlands
Human Behavior and Organisational Innovations, TNO, P.O. Box 23, 3769 ZG Soesterberg, The Netherlands
Cybersecurity & SMEs Research Group, The Hague University of Applied Sciences, P.O. Box 13336, 2501 EH The Hague, The Netherlands
Author to whom correspondence should be addressed.
Multimodal Technol. Interact. 2019, 3(3), 62;
Received: 28 June 2019 / Revised: 4 September 2019 / Accepted: 6 September 2019 / Published: 15 September 2019
(This article belongs to the Special Issue Text Mining in Complex Domains)
The number of cyberattacks on organizations is growing. To increase cyber resilience, organizations need to obtain foresight to anticipate cybersecurity vulnerabilities, developments, and potential threats. This paper describes a tool that combines state of the art text mining and information retrieval techniques to explore the opportunities of using these techniques in the cybersecurity domain. Our tool, the Horizon Scanner, can scrape and store data from websites, blogs and PDF articles, and search a database based on a user query, show textual entities in a graph, and provide and visualize potential trends. The aim of the Horizon Scanner is to help experts explore relevant data sources for potential threats and trends and to speed up the process of foresight. In a requirements session and user evaluation of the tool with cyber experts from the Dutch Defense Cyber Command, we explored whether the Horizon Scanner tool has the potential to fulfill its aim in the cybersecurity domain. Although the overall evaluation of the tool was not as good as expected, some aspects of the tool were found to have added value, providing us with valuable insights into how to design decision support for forecasting analysts. View Full-Text
Keywords: information retrieval; foresight; digital crime; cyber security; cyber resilience; trend analysis information retrieval; foresight; digital crime; cyber security; cyber resilience; trend analysis
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MDPI and ACS Style

de Boer, M.H.T.; Bakker, B.J.; Boertjes, E.; Wilmer, M.; Raaijmakers, S.; van der Kleij, R. Text Mining in Cybersecurity: Exploring Threats and Opportunities. Multimodal Technol. Interact. 2019, 3, 62.

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