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Anticipating Cyberdefense Capability Requirements by Link Prediction Analysis

In: Cyberdefense

Author

Listed:
  • Santiago Anton Moreno

    (Swiss Federal Institute of Technology Lausanne)

  • Dimitri Percia David

    (University of Applied Sciences Valais)

  • Alain Mermoud

    (Cyber-Defence Campus, armasuisse Science and Technology)

  • Thomas Maillart

    (University of Geneva)

  • Anita Mezzetti

    (Swiss Federal Institute of Technology Lausanne, Section of Financial Engineering)

Abstract

Job offers reveal employer preferences about capabilities required for future cyberdefense. We model such job openings as edges of a bipartite network of organizations and technologies. We propose and train a parsimonious prediction algorithm with extant job offer data to predict which capabilities firms will require up to six months from now. We compare the efficiency of our method across several unsupervised learning similarity-based algorithms and a supervised learning method to optimize model dynamics.

Suggested Citation

  • Santiago Anton Moreno & Dimitri Percia David & Alain Mermoud & Thomas Maillart & Anita Mezzetti, 2023. "Anticipating Cyberdefense Capability Requirements by Link Prediction Analysis," International Series in Operations Research & Management Science, in: Marcus Matthias Keupp (ed.), Cyberdefense, chapter 0, pages 135-145, Springer.
    • Handle: RePEc:spr:isochp:978-3-031-30191-9_9
    DOI: 10.1007/978-3-031-30191-9_9
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