Securing Healthcare IoT Ecosystems: Anomaly Detection and Resilience Mechanisms for Enhanced Data Privacy and Trustworthiness

<ns3:p>The propagation of Internet of Things (IoT) devices in healthcare, while remote monitoring has enabled and patient care has improved, significant vulnerabilities have introduced that threaten the security of sensitive medical data. To address this crucial challenge, the study proposes a novel framework to enhance the privacy of data and trustworthiness in healthcare IoT ecosystems using advanced anomaly detection. We introduced a Customized Sand Cat Swarm driven Updated Random Forest (CSCS-URF) model, as a first step, data is preprocessed using min-max normalization and Recursive Feature Elimination (RFE). The Second step is analyzing data by an ensemble classifier optimized via a customized swarm intelligence algorithm to effectively identify security anomalies. Then, evaluate the system on the CICIoT20232 dataset, the proposed CSCS-URF method shows superior performance compared to existing benchmarks, achieving an accuracy of 97%, precision of 94%, recall of 96%, and an F1-score of 95%. These results indicate that the CSCS-URF framework is a robust solution for proactive security, enabling the early detection of potential breaches to strengthen system integrity and safeguard patient data against evolving cyber threats in the healthcare sector.</ns3:p>