Machine learning for the security of healthcare systems based on Internet of Things and edge computing

Abstract

Using the Internet of Medical Things (IoMT) for treatment and diagnosis has exponentially grown due to its diverse use cases and efficient planning with defined resources. IoMT in the e-healthcare system enables continuous monitoring of a patient's medical indicators, which eases routine patient follow-ups and increases the productivity of human life. Nowadays, biomedical data can be easily collected from patients remotely thanks to the integration of wireless communications, wearable devices, and big data. In edge computing-based healthcare applications, the importance of privacy protection is increasing because of the openness and data sensitivity of communication channels. Dealing with the privacy and security issues for medical devices used for real-time processing and analysis is necessary through big data analysis. In this chapter, we investigate the security risks of big data platforms in health care and how machine learning can mitigate security risks.