Anticipating Cyberdefense Capability Requirements by Link Prediction Analysis

Santiago Anton Moreno (), Dimitri Percia David (), Alain Mermoud (), Thomas Maillart () and Anita Mezzetti
Additional contact information
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

Chapter Chapter 9 in Cyberdefense, 2023, pp 135-145 from Springer

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-031-30191-9_9

Ordering information: This item can be ordered from
http://www.springer.com/9783031301919

DOI: 10.1007/978-3-031-30191-9_9

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().