TransformerXL-GAN-Based Secure Forecasting Architecture for Mitigating Algal Bloom-Induced Sensor Spoofing in Marine IoT

The growing reliance on Internet of Things (IoT) networks in marine environments has highlighted concerns about sensor spoofing attacks, particularly under the disruptive influence of harmful algal blooms (HABs). These blooms introduce considerable biological noise and sensor drift, creating a challenging environment where distinguishing between genuine environmental anomalies and deliberate adversarial attacks becomes difficult. This study tackles the pressing issue of spoofing detection in marine IoT systems affected by bloom-induced interference, aiming to offer a solution that is both accurate and interpretable. The proposed TransformerXL-GAN with explainable artificial intelligence (XAI) framework combines TransformerXL’s long-term temporal modeling with GAN’s adversarial robustness while incorporating SHapley Additive exPlanations (SHAP)-based interpretability for clearer decision-making insights. Additional strategies, such as event-aware data augmentation, temporal anomaly filtering, and hybrid Cascaded Hyperparameter–Multi-Stage Adaptation (CH-MSA) optimization, further bolster learning stability and detection precision. Validation is conducted using the MARSS-2025 dataset, a comprehensive collection of marine sensor readings from the Norwegian Institute for Water Research. Experimental outcomes reveal that the TransformerXL-GAN+XAI framework surpasses both traditional and cutting-edge deep learning methods, achieving an impressive 98.8% accuracy and an AUC of 0.995. An accompanying ablation study underscores the collective benefit of each architectural component. These findings demonstrate the framework’s potential to deliver reliable, transparent, and biologically adaptive spoofing detection in complex marine IoT environments.