Internet of Things devices are increasingly embedded in elderly care services, expanding exposure to network intrusions that can disrupt remote monitoring and compromise sensitive data. This study develops a stacked deep-learning intrusion detection meta-model for elderly care network settings and evaluates it using the Network Security Laboratory–Knowledge Discovery and Data Mining (NSL-KDD) and Canadian Institute for Cybersecurity Intrusion Detection System 2018 (CICIDS2018) datasets. The approach integrates deep neural networks, convolutional neural networks, recurrent neural networks with long short-term memory and gated recurrent units, and autoencoders by fusing their calibrated decision outputs in a second-stage learner. Data preprocessing included encoding of categorical attributes, normalization, and class-imbalance handling, with model comparison performed using five-fold cross-validation and one-way analysis of variance with Tukey’s post hoc contrasts. The proposed meta-model achieved 99.85% accuracy, 99.2% precision, 99.1% recall, and a 99.15% F1 score, exceeding individual base learners and comparator ensembles, and showed strong detection for frequent service-disruption and reconnaissance attacks while remaining less sensitive to rare exploit categories (approximately 0.85 precision/recall for low-support classes). These results indicate that decision-level fusion can improve robustness under class imbalance and supports low-latency deployment in resource-constrained care facilities when implemented in an edge–cloud monitoring workflow.
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