Resilient Trust Management via Adaptive Threat Modeling in Multi-Agent Networks

Abstract

Decentralized multi-agent environments increasingly rely on trust-aware coordination mechanisms to regulate cooperation and resource sharing among autonomous participants. However, existing trust and reputation frameworks often suffer from limited adaptability when confronted with strategic manipulation, collusion, and coordinated misinformation attacks. This paper proposes an integrated evaluation and risk modeling framework for distributed trust systems based on game-theoretic behavior analysis and adversarial simulation. Instead of relying solely on static reputation aggregation rules, the proposed platform constructs dynamic interaction graphs and behavioral state models to capture evolving incentive structures and attack strategies. The framework incorporates adaptive threat profiling and stochastic agent modeling to emulate a wide range of rational, opportunistic, and adversarial behaviors. Through iterative scenario generation and reinforcement-driven policy evolution, the system enables large-scale stress testing of trust mechanisms under complex network conditions. To validate the effectiveness of the proposed approach, multiple experimental studies are conducted on representative distributed coordination tasks, including service allocation and cooperative sensing. Results demonstrate that the platform can accurately identify structural vulnerabilities, quantify systemic risk levels, and provide actionable insights for trust mechanism optimization. The proposed methodology offers a scalable and flexible tool for designing resilient trust management architectures in next-generation decentralized multi-agent systems.