Dynamic Psychological Game Theory for Secure Internet of Battlefield Things (IoBT) Systems

TL;DR

This paper introduces a dynamic psychological game model for securing IoBT systems against jamming, using Bayesian learning to optimize soldier strategies and improve mission success rates.

Contribution

It develops a novel psychological game framework and a Bayesian learning algorithm to enhance security in IoBT systems under adversarial conditions.

Findings

Soldier's payoff increased by up to 15.11% with the proposed method.

The psychological game approach effectively models adversarial intentions.

Bayesian updating converges to a psychological equilibrium.

Abstract

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.