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Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions

Author

Listed:
  • Jules Sadefo Kamdem

    (MRE - Montpellier Recherche en Economie - UM - Université de Montpellier)

  • Danielle Selambi

    (African Institute for Mathematical Sciences (AIMS-Cameroon))

Abstract

In this paper, we estimate the cost of a data breach using the number of compromised records. The number of such records is predicted by means of a machine learning model, particularly the Random Forest. We further analyse the fat tail phenomena which capture the underlying dynamics in the number of affected records. The objective is to calculate the maximum loss in order to answer the question of the insurability of cyber risk. Our results show that the total number of affected records follow a Frechet distribution, and we then estimate the Generalized Extreme Value (GEV) parameters to calculate the value at risk (VaR). This analysis is critical because it gives an idea of the maximum loss that can be generated by an enterprise data breach. These results are usable in anticipating the premiums for cyber risk coverage in the insurance markets.

Suggested Citation

  • Jules Sadefo Kamdem & Danielle Selambi, 2022. "Cyber-Risk Forecasting using Machine Learning Models and Generalized Extreme Value Distributions," Working Papers hal-03814979, HAL.
    • Handle: RePEc:hal:wpaper:hal-03814979
    Note: View the original document on HAL open archive server: https://hal.science/hal-03814979
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    References listed on IDEAS

    as
    1. Farkas, Sébastien & Lopez, Olivier & Thomas, Maud, 2021. "Cyber claim analysis using Generalized Pareto regression trees with applications to insurance," Insurance: Mathematics and Economics, Elsevier, vol. 98(C), pages 92-105.
    2. Martin Eling & Werner Schnell, 2016. "What do we know about cyber risk and cyber risk insurance?," Journal of Risk Finance, Emerald Group Publishing Limited, vol. 17(5), pages 474-491, November.
    3. Gérard Biau & Erwan Scornet, 2016. "A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 197-227, June.
    4. Gérard Biau & Erwan Scornet, 2016. "Rejoinder on: A random forest guided tour," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 25(2), pages 264-268, June.
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Cyber insurance; Cyber risk; Machine Learning; Regression Trees; Random Forest; Generalized Extreme Value;
    All these keywords.

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