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In this paper, the problem of misinformation propagation is studied for anInternet of Battlefield Things (IoBT) system in which an attacker seeks toinject false information in the IoBT nodes in order to compromise the IoBToperations. In the considered model, each IoBT node seeks to counter themisinformation attack by finding the optimal probability of accepting a giveninformation that minimizes its cost at each time instant. The cost is expressedin 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 issuitable to model a massive heterogeneous IoBT system. For this game, themean-field equilibrium is characterized, and an algorithm based on the forwardbackward sweep method is proposed to find the mean-field equilibrium. Then, thefinite IoBT case is considered, and the conditions of convergence of the Nashequilibria in the finite case to the mean-field equilibrium are presented.Numerical results show that the proposed scheme can achieve a 1.2-fold increasein the quality of information (QoI) compared to a baseline scheme in which theIoBT nodes are always transmitting. The results also show that the proposedscheme can reduce the proportion of infected nodes by 99% compared to thebaseline.

A Multiclass Mean-Field Game for Thwarting Misinformation Spread in the Internet of Battlefield Things (IoBT)