Integrity Shield: Ensuring Real-time Data Integrity in Healthcare IoT with Isolation Forest Anomaly Detection
Keywords:
Healthcare, Internet of things, Machine learning, SVMAbstract
The healthcare sector has been greatly transformed by the Internet of Things (IoT) which brings opportunities, for monitoring and management of patient health. However, there are challenges in ensuring the reliability and authenticity of the amount of healthcare data transmitted through IoT devices. In this paper we suggest an approach called” Machine Learning Based Data Integrity Assurance for Healthcare IoT” to tackle these challenges. Our proposed algorithm utilizes machine learning techniques to detect anomalies and potential tampering attempts in time thus guaranteeing the trustworthiness and dependability of healthcare data collected from IoT devices. By establishing data profiles and continuously monitoring data streams our algorithm can adjust to evolving data patterns. Promptly identify any issues related to data integrity. Moreover, through trust-based data fusion our algorithm takes into account the trust level associated with each device in order to appropriately assess their contributions. With its adaptability, scalability and cost effectiveness our solution holds promise in enhancing the security and integrity of healthcare data, within IoT based healthcare systems.
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