B0926
Title: Building up cyber resilience by better grasping cyber risk via a new algorithm for modelling heavy-tailed data
Authors: Michel Dacorogna - Prime Re Solutions (Switzerland) [presenting]
Abstract: Cyber security and resilience are major challenges in modern economies; this is why they are top priorities on the agenda of governments, security and defence forces, and the management of companies and organizations. Hence, there is a need for a deep understanding of cyber risks to improve resilience. An analysis of the database of the cyber complaints filed at the Gendarmerie Nationale is proposed. The analysis is performed with a new algorithm developed for non-negative asymmetric heavy-tailed data, which could become a handy tool for applied fields, including operations research. This method gives a good estimation of the full distribution, including the tail. The study confirms the finiteness of the loss expectation, a necessary condition for insurability. Finally, the consequences of this model are drawn for risk management, its results are compared to other standard EVT models, and the ground for the classification of attacks is laid based on the fatness of the tail.
