Authors :
Omolola Akinola; Akintunde Akinola; Ifenna Victor Ifeanyi; Omowunmi Oyerinde; Oyedele Joseph Adewole; Busola Sulaimon; Busola Sulaimon
Volume/Issue :
Volume 9 - 2024, Issue 3 - March
Google Scholar :
https://tinyurl.com/3zyjmjp4
Scribd :
https://tinyurl.com/khsrkey9
DOI :
https://doi.org/10.38124/ijisrt/IJISRT24MAR1231
Abstract :
The Internet of Medical Things (IoMT) has
begun functioning like this: improved patient monitoring
and an easily accessible digital data warehouse. Despite
that, this methodology of the internet will potentially have
a counter balance which risks for patient data might
involve hacking, data theft, and unauthorized access that
may contain great consequences for patient privacy and
safety. This article examines the possibility of utilizing
new AI technology, including inter alia deep learning,
unsupervised learning, and ensemble learning to further
boost anomaly detection and threat management in
connected cloud medical systems. Many old rules and
approaches based on statistics lose relevancy versus the
dynamics and unpredictability of modern cyberattacks.
Identification of anomalies in cyber security is nearly
unavoidable, and it should be the first and the last
reaction for detecting irregularities in behavior that may
indicate undesirable acts or attacks. The paper aims at
understanding how AI/ML approaches can give more
sophisticated and versatile interventions for finding out
anomalies in cloud-attached medical machines.
Moreover, this research details robust AI/ML methods
such as the adversarial machine learning and
reinforcement learning for a perfect threat mitigation.
These techniques which activates machine learning
models to learn from data continuing to adjust to new
evolving threats and then to establish intelligent and
proactive threat response systems. The data experiment,
which focuses on relevant data sets, reveals that it is the
AI/ML techniques that possess the upper hand over
traditional methods when it comes to identifying
anomalies and defending against threats for cloud-
connected medical devices. Such finding expresses much
significance for the healthcare industry, as it gives room
for the inclusion of AI/ML techniques into the security
systems of the medical devices, which are all connected to
the cloud. Through the employment of these strategies,
healthcare units will become better able to detect and halt
any form of threat and as a consequence patients’ data
will be protected, devices will continue operating
effectively, and eventually patients’ safety and healthcare
units will benefit and gain trust from patients.
Keywords :
Cloud
The Internet of Medical Things (IoMT) has
begun functioning like this: improved patient monitoring
and an easily accessible digital data warehouse. Despite
that, this methodology of the internet will potentially have
a counter balance which risks for patient data might
involve hacking, data theft, and unauthorized access that
may contain great consequences for patient privacy and
safety. This article examines the possibility of utilizing
new AI technology, including inter alia deep learning,
unsupervised learning, and ensemble learning to further
boost anomaly detection and threat management in
connected cloud medical systems. Many old rules and
approaches based on statistics lose relevancy versus the
dynamics and unpredictability of modern cyberattacks.
Identification of anomalies in cyber security is nearly
unavoidable, and it should be the first and the last
reaction for detecting irregularities in behavior that may
indicate undesirable acts or attacks. The paper aims at
understanding how AI/ML approaches can give more
sophisticated and versatile interventions for finding out
anomalies in cloud-attached medical machines.
Moreover, this research details robust AI/ML methods
such as the adversarial machine learning and
reinforcement learning for a perfect threat mitigation.
These techniques which activates machine learning
models to learn from data continuing to adjust to new
evolving threats and then to establish intelligent and
proactive threat response systems. The data experiment,
which focuses on relevant data sets, reveals that it is the
AI/ML techniques that possess the upper hand over
traditional methods when it comes to identifying
anomalies and defending against threats for cloud-
connected medical devices. Such finding expresses much
significance for the healthcare industry, as it gives room
for the inclusion of AI/ML techniques into the security
systems of the medical devices, which are all connected to
the cloud. Through the employment of these strategies,
healthcare units will become better able to detect and halt
any form of threat and as a consequence patients’ data
will be protected, devices will continue operating
effectively, and eventually patients’ safety and healthcare
units will benefit and gain trust from patients.
