CAROLINE HILLAIRET | Professor and Director of the Actuarial Science engineering track and Advanced Master, ENSAE and CREST
OLIVIER LOPEZ | Professor of Applied Mathematics (Statistics), Laboratoire de Probabilités, Statistique et Modélisation, Sorbonne Université
This paper proposes a stochastic model to simulate massive cyberattack scenarios, taking into account the structure of the network as well as partial or full protection measures. Events, such as the recent COVID-19 pandemic, can rapidly generate consequent damages, and mutualization of the losses may not hold anymore. The framework is based on the multigroup SIR (susceptible, infected, and recovered) epidemiological model, which can be calibrated from a relatively small amount of data and through fast numerical procedures. As an illustration, we replicate the impact of a Wannacrytype event using a connectivity network inferred from macroeconomic data of the OECD. We show how this model can be used to generate reasonable scenarios of cyber events, and investigate the response to different types of attacks or behavior of the actors, allowing for the quantification of the benefits of an efficient prevention policy.