  Internet of Things (IoT) suffers from vulnerable sensor nodes, which are
likely to endure data falsification attacks following physical or cyber
capture. Moreover, centralized decision-making and data fusion schemes commonly
used by these networks turn these decision points into single points of
failure, which are likely to be exploited by smart attackers. In order to face
this serious security thread, we propose a novel scheme that enables
distributed data aggregation and decision-making by following social learning
principles. Our proposed scheme makes sensor nodes to act resembling the
manners of agents within a social network. We analytically examine how local
actions of individual agents can propagate through the whole network, affecting
the collective behaviour. Finally, we show how social learning can enable
network resilience against data falsification attacks, even when a significant
portion of the nodes have been compromised by the adversary.
