  In this paper, the problem of misinformation propagation is studied for an
Internet of Battlefield Things (IoBT) system in which an attacker seeks to
inject false information in the IoBT nodes in order to compromise the IoBT
operations. In the considered model, each IoBT node seeks to counter the
misinformation attack by finding the optimal probability of accepting a given
information that minimizes its cost at each time instant. The cost is expressed
in terms of the quality of information received as well as the infection cost.
The problem is formulated as a mean-field game with multiclass agents which is
suitable to model a massive heterogeneous IoBT system. For this game, the
mean-field equilibrium is characterized, and an algorithm based on the forward
backward sweep method is proposed to find the mean-field equilibrium. Then, the
finite IoBT case is considered, and the conditions of convergence of the Nash
equilibria in the finite case to the mean-field equilibrium are presented.
Numerical results show that the proposed scheme can achieve a 1.2-fold increase
in the quality of information (QoI) compared to a baseline scheme in which the
IoBT nodes are always transmitting. The results also show that the proposed
scheme can reduce the proportion of infected nodes by 99% compared to the
baseline.
