Enhancing Online Statistical Decision-Making in Maritime C2 Systems: A Resilience Analysis of the LORD Procedure Under Adversarial Data Perturbations

Real-time statistical inference plays a pivotal role in maritime Command and Control (C2) environments, particularly for applications such as satellite-based object detection and underwater signal interpretation. These contexts often require online multiple hypothesis testing mechanisms capable of sequential decision-making while preserving statistical rigor. A primary concern is the control of the False Discovery Rate (FDR), as erroneous detections can impair operational effectiveness. In this study, we investigate the robustness of the Levels based On Recent Discovery (LORD) algorithm under adversarial conditions by introducing controlled perturbations to the data stream—specifically, missing or corrupted p-values derived from simulated Gaussian distributions. Inspired by developments in corruption-aware multi-armed bandit models, we formulate adversarial scenarios and propose defense strategies that modify the LORD algorithm’s threshold sequence and integrate an online Benjamini–Hochberg procedure. The results, based on extensive Monte Carlo simulations, demonstrate that even a single missing p-value can trigger a cascading effect that reduces statistical power, and that our proposed mitigation strategies significantly improve algorithmic resilience while maintaining FDR control. These contributions advance the development of robust online statistical decision-making tools for real-time maritime surveillance systems operating under uncertain and error-prone conditions.