 Epidemiology inspired Cybersecurity Threats Forecasting Models applied to e-GovernmentJean Langlois-Berthelot, Christophe Gaie and Jean-Fabrice Lebraty Post-Print from HAL 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. Keywords: Epidemiology; Forecasting; Cybersecurity; SEIR; Herd Immunity; e-Government; Algorithm; Digital Identity (search for similar items in EconPapers) Published in Transforming Public Services—Combining Data and Algorithms to Fulfil Citizen’s Expectations, 252, Springer Nature Switzerland, pp.151-174, 2024, Intelligent Systems Reference Library, 978-3-031-55574-9. ⟨10.1007/978-3-031-55575-6⟩ There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:halshs-04568760 DOI: 10.1007/978-3-031-55575-6 Access Statistics for this paper More papers in Post-Print from HAL |
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