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Epidemiology inspired Cybersecurity Threats Forecasting Models applied to e-Government

Author

Listed:
  • Jean Langlois-Berthelot
  • Christophe Gaie
  • Jean-Fabrice Lebraty

    (Laboratoire de Recherche Magellan - UJML - Université Jean Moulin - Lyon 3 - Université de Lyon - Institut d'Administration des Entreprises (IAE) - Lyon)

Abstract

This chapter delves into the innovative fusion of epidemiology and cybersecurity, presenting a novel paradigm for forecasting cybеr threats with applications for e-Government. Drawing inspiration from epidemiological models that predict the spread of diseases, we propose pionееring approaches to anticipate and mitigate cybеr threats in the digital governance landscape. To enhance the robustness of cyberattack forecasting, the chapter explores ensemble methods that combine predictions from multiple epidemiology models. This approach aims to mitigate individual model biases and improve forecasting accuracy. It also outlines that human expertise is required to contextualize the forecasts, identifying potential outliers, and define cybersecurity strategies. In conclusion, this chapter provides a comparison of the proposed models and identifies future challenges to enhance cybersecurity of e-Government.

Suggested Citation

  • Jean Langlois-Berthelot & Christophe Gaie & Jean-Fabrice Lebraty, 2024. "Epidemiology inspired Cybersecurity Threats Forecasting Models applied to e-Government," Post-Print halshs-04568760, HAL.
    • Handle: RePEc:hal:journl:halshs-04568760
    DOI: 10.1007/978-3-031-55575-6
    as

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