Identification of Future Cyberdefense Technology by Text Mining

Dimitri Percia David (), William Blonay (), Sébastien Gillard (), Thomas Maillart (), Alain Mermoud (), Loïc Maréchal () and Michael Tsesmelis ()
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Dimitri Percia David: University of Applied Sciences Valais
William Blonay: armasuisse Science and Technology
Sébastien Gillard: Military Academy at ETH Zurich
Thomas Maillart: Université de Genève
Alain Mermoud: armasuisse Science and Technology
Loïc Maréchal: HEC lausanne, University of Lausanne
Michael Tsesmelis: armasuisse Science and Technology

Chapter Chapter 5 in Cyberdefense, 2023, pp 69-86 from Springer

Abstract: Abstract We propose a reproducible, automated, scalable, and free method for automated bibliometric analysis that requires little computing power. We explain how firms can use this method with open source data from public repositories to generate unbiased insights about future technology developments, and to assess the maturity, security and likely future development of particular technology domains. The method is demonstrated by systematic text mining of more than 400,000 e-prints from the arXiv repository.

Date: 2023
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DOI: 10.1007/978-3-031-30191-9_5

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