  In this paper, a novel anti-jamming mechanism is proposed to analyze and
enhance the security of adversarial Internet of Battlefield Things (IoBT)
systems. In particular, the problem is formulated as a dynamic psychological
game between a soldier and an attacker. In this game, the soldier seeks to
accomplish a time-critical mission by traversing a battlefield within a certain
amount of time, while maintaining its connectivity with an IoBT network. The
attacker, on the other hand, seeks to find the optimal opportunity to
compromise the IoBT network and maximize the delay of the soldier's IoBT
transmission link. The soldier and the attacker's psychological behavior are
captured using tools from psychological game theory, with which the soldier's
and attacker's intentions to harm one another are considered in their
utilities. To solve this game, a novel learning algorithm based on Bayesian
updating is proposed to find a $\epsilon$-like psychological self-confirming
equilibrium of the game. Simulation results show that, based on the error-free
beliefs on the attacker's psychological strategies and beliefs, the soldier's
material payoff can be improved by up to 15.11\% compared to a conventional
dynamic game without psychological considerations.
