<![CDATA[Recently, wireless sensor networks (WSNs) have been widely integrated in critical applications such as environmental monitoring, smart cities, and modern healthcare for remote patient monitoring and data collection. This makes WSNs increasingly susceptible to security threats, including eavesdropping, jamming, sybil, data injection, routing, senor node capture, malicious intrusion attacks etc., therefore maintaining integrity, confidentiality, and availability of sensitive data and preserving privacy become a challenge. Existing mechanisms do not integrate threat detection, privacy preservation, and adaptability to evolving threats leading to security breaches in the left-out security requirements. This paper proposes an ensemble-based threat detection mechanism (FAL-ELeM-IDS) with privacy-awareness and adaptability to evolving threats for WSNs-based healthcare systems. The ensemble consists of Online Random Forest, Online AdaBoost, Support Vector Machine, Neural Network, and XGBoost to ensure detection high accuracy and low false-positives. Federated Learning combined with ensemble technique to provide confidentiality and a combined Online Adaptive Boosting and Online Random Forests algorithms to provide adaptability. The proposed model trained on a real-world healthcare sensor dataset demonstrates its superiority in performance compared to conventional models. An accuracy of 97.8%, a recall of 97%, precision of 98%, and F1-score of 97.5%, was achieved outperforming individual models by significant margins, showing that the model is accurate and reliable in detecting threats. This mechanism implies enhanced system security and privacy, timely threat mitigation ensuring patient safety, and boost in public acceptance for sensor-based healthcare services. Overall, this work contributes a scalable, privacy-aware, and adaptive threat detection mechanism suitable for integration in the sensitive healthcare applications.]]>
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