  A common assumption in the social learning literature is that agents exchange
information in an unselfish manner. In this work, we consider the scenario
where a subset of agents aims at deceiving the network, meaning they aim at
driving the network beliefs to the wrong hypothesis. The adversaries are
unaware of the true hypothesis. However, they will "blend in" by behaving
similarly to the other agents and will manipulate the likelihood functions used
in the belief update process to launch inferential attacks. We will
characterize the conditions under which the network is misled. Then, we will
explain that it is possible for such attacks to succeed by showing that
strategies exist that can be adopted by the malicious agents for this purpose.
We examine both situations in which the agents have access to information about
the network model as well as the case in which they do not. For the first case,
we show that there always exists a way to construct fake likelihood functions
such that the network is deceived regardless of the true hypothesis. For the
latter case, we formulate an optimization problem and investigate the
performance of the derived attack strategy by establishing conditions under
which the network is deceived. We illustrate the learning performance of the
network in the aforementioned adversarial setting via simulations. In a
nutshell, we clarify when and how a network is deceived in the context of
non-Bayesian social learning.
