  The integration of Internet of Things (IoT) devices in healthcare
applications has revolutionized patient care, monitoring, and data management.
The Global IoT in Healthcare Market value is $252.2 Billion in 2023. However,
the rapid involvement of these devices brings information security concerns
that pose critical threats to patient privacy and the integrity of healthcare
data. This paper introduces a novel machine learning (ML) based architecture
explicitly designed to address and mitigate security vulnerabilities in IoT
devices within healthcare applications. By leveraging advanced convolution ML
architecture, the proposed architecture aims to proactively monitor and detect
potential threats, ensuring the confidentiality and integrity of sensitive
healthcare information while minimizing the cost and increasing the portability
specialized for healthcare and emergency environments. The experimental results
underscore the accuracy of up to 93.6% for predicting various attacks based on
the results demonstrate a zero-day detection accuracy simulated using the
CICIoT2023 dataset and reduces the cost by a factor of x10. The significance of
our approach is in fortifying the security posture of IoT devices and
maintaining a robust implementation of trustful healthcare systems.
