  In novel forms of the Social Internet of Things, any mobile user within
communication range may help routing messages for another user in the network.
The resulting message delivery rate depends both on the users' mobility
patterns and the message load in the network. This new type of configuration,
however, poses new challenges to security, amongst them, assessing the effect
that a group of colluding malicious participants can have on the global message
delivery rate in such a network is far from trivial. In this work, after
modeling such a question as an optimization problem, we are able to find quite
interesting results by coupling a network simulator with an evolutionary
algorithm. The chosen algorithm is specifically designed to solve problems
whose solutions can be decomposed into parts sharing the same structure. We
demonstrate the effectiveness of the proposed approach on two medium-sized
Delay-Tolerant Networks, realistically simulated in the urban contexts of two
cities with very different route topology: Venice and San Francisco. In all
experiments, our methodology produces attack patterns that greatly lower
network performance with respect to previous studies on the subject, as the
evolutionary core is able to exploit the specific weaknesses of each target
configuration.
