A machine learning based approach to identifying malicious activity to improve privacy in IoT-based intelligent healthcare monitoring system
Abstract
As the use of the Internet of Things (IoT) grows exponentially in the medical field, one of the biggest concerns is the safety of patients’ personal health information. Leveraging IoT technology in a modern healthcare environment facilitates precise handling of data and patient monitoring. Healthcare systems are susceptible to security hazards and attacks. The primary objective of malicious operations targeting these systems is to compromise privacy and obtain unauthorized access to internal processes. Consequently, advanced analytics can strengthen IoT security as a whole by facilitating the detection, mitigation, and prevention of such intrusions. The fulfilment of security requirements is crucial for improving the current healthcare system with IoT technologies, and real-world applications can benefit greatly from Machine learning (ML) applications running on authentic datasets. This paper provides framework for detecting malicious activities occurred during data transmission in IoT based health monitoring system using ML approach. In proposed framework we enhanced decision tree algorithm by utilizing oversampling and fine-tuning during training of model. The proposed framework has been analysed using real dataset that contains IoT device data transmission activities that may contain activities generated by malicious nodes. The proposed mechanism achieved an accuracy of 99.6% from the perspective of other compared ML approaches.
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DOI: https://doi.org/10.32629/jai.v7i5.1492
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