  Cyber security and resilience are major challenges in our modern economies;
this is why they are top priorities on the agenda of governments, security and
defense forces, management of companies and organizations. Hence, the need of a
deep understanding of cyber risks to improve resilience. We propose here an
analysis of the database of the cyber complaints filed at the {\it Gendarmerie
Nationale}. We perform this analysis with a new algorithm developed for
non-negative asymmetric heavy-tailed data, which could become a handy tool in
applied fields. This method gives a good estimation of the full distribution
including the tail. Our study confirms the finiteness of the loss expectation,
necessary condition for insurability. Finally, we draw the consequences of this
model for risk management, compare its results to other standard EVT models,
and lay the ground for a classification of attacks based on the fatness of the
tail.
