  Computer viruses are evolving by developing spreading mechanisms based on the
use of multiple vectors of propagation. The use of the social network as an
extra vector of attack to penetrate the security measures in IP networks is
improving the effectiveness of malware, and have therefore been used by the
most aggressive viruses, like Conficker and Stuxnet. In this work we use
interdependent networks to model the propagation of these kind of viruses. In
particular, we study the propagation of a SIS model on interdependent networks
where the state of each node is layer-independent and the dynamics in each
network follows either a contact process or a reactive process, with different
propagation rates. We apply this study to the case of existing multilayer
networks, namely a Spanish scientific community of Statistical Physics, formed
by a social network of scientific collaborations and a physical network of
connected computers in each institution. We show that the interplay between
layers increases dramatically the infectivity of viruses in the long term and
their robustness against immunization.
