Abstract
We propose a resilient framework for the mitigation of misinformation epidemics within dynamic information ecosystems under operational latencies and adversarial telemetry corruption. A dual-layer control architecture that balances platform-level usability with hard safety constraints is designed. The framework utilizes a polyhedral backward induction scheme to synthesize a verified controlled invariant cover. This geometric formulation guarantees that node-level infodemic penetration levels remain strictly bounded within a designated safe target set. To counter coordinated false data injection (FDI) attacks on state reporting channels, we integrate an online state observer, utilizing a private physical watermarking sequence, Δw(t). This mechanism creates an asymmetric information structure that exposes stealthy evasion tactics through a Chi-Squared (X^2) tracking residual monitor. Parametric sensitivity profiling maps the operational boundaries of the network, isolating the primary destabilizing role of virality (B) alongside the primary stabilizing lever of intervention effectiveness (k). Empirical validation conducted on synthetic networks and the ESOC COVID-19 Misinformation Dataset demonstrates that the self-triggered adaptive control law consistently outperforms baseline implementations, yielding a platform usability cost reduction between 38.5% and 53.8% while maintaining absolute safety integrity. These results establish the framework as a robust tool for securing critical information infrastructure against sophisticated, coordinated manipulation.