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.

Research Paper

Closed Access research paper available here: Identification of Future Cyberdefense Technology by Text Mining.

BibTex

@Inbook{PerciaDavid2023,
author="Percia David, Dimitri
and Blonay, William
and Gillard, S{\'e}bastien
and Maillart, Thomas
and Mermoud, Alain
and Mar{\'e}chal, Lo{\"i}c
and Tsesmelis, Michael",
editor="Keupp, Marcus Matthias",
title="Identification of Future Cyberdefense Technology by Text Mining",
bookTitle="Cyberdefense: The Next Generation",
year="2023",
publisher="Springer International Publishing",
address="Cham",
pages="69--86",
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.",
isbn="978-3-031-30191-9",
doi="10.1007/978-3-031-30191-9_5",
url="https://doi.org/10.1007/978-3-031-30191-9_5"
}