Intrusion detection in Internet of Things (IoT) environments presents challenges due to heterogeneous devices, diverse attack vectors, and highly imbalanced datasets. Existing research on the ToN-IoT dataset has largely emphasized binary classification and single-model pipelines, which often show strong performance but limited generalizability, probabilistic reliability, and operational interpretability. This study proposes a stacked ensemble deep learning framework that integrates random forest, extreme gradient boosting, and a deep neural network as base learners, with CatBoost as the meta-learner. On the ToN-IoT Linux process dataset, the model achieved near-perfect discrimination (macro area under the curve = 0.998), robust calibration, and superior F1-scores compared with standalone classifiers. Interpretability was achieved through SHapley Additive exPlanations–based feature attribution, which highlights actionable drivers of malicious behavior, such as command-line patterns, process scheduling anomalies, and CPU usage spikes, and aligns these indicators with MITRE ATT&CK tactics and techniques. Complementary analyses, including cumulative lift and sensitivity-specificity trade-offs, revealed the framework’s suitability for deployment in security operations centers, where calibrated risk scores, transparent explanations, and resource-aware triage are essential. These contributions bridge methodological rigor in artificial intelligence/machine learning with operational priorities in cybersecurity, delivering a scalable and explainable intrusion detection system suitable for real-world deployment in IoT environments.