Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices

Authors

  • Omolola Akinola Dept. of Information Systems and Analysis Lamar University Beaumont, Texas, USA
  • Akintunde Akinola Department of Accounting and Finance, Ekiti State University
  • Ifenna Victor Ifeanyi Dept. of Industrial Engineering, Lamar University Beaumont, Texas, USA
  • Omowunmi Oyerinde MIS Lamar University Beaumont, Texas
  • Oyedele Joseph Adewole Dept. of Industrial and Systems Engineering, Lamar University Beaumont, Texas, USA
  • Busola Sulaimon Dept of Industrial and System Engineering, Lamar University Beaumont, Texas USA
  • Basirat Oyekan Oyekan MIS Lamar University Beaumont, Texas, USA

DOI:

https://doi.org/10.38124/ijsrmt.v3i3.26

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.

 

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Published

2024-03-20

How to Cite

Akinola , O., Akinola, A., Victor Ifeanyi, I., Oyerinde , O., Joseph Adewole , O., Sulaimon, B., & Oyekan, B. O. (2024). Artificial Intelligence and Machine Learning Techniques for Anomaly Detection and Threat Mitigation in Cloud-Connected Medical Devices. International Journal of Scientific Research and Modern Technology, 3(3). https://doi.org/10.38124/ijsrmt.v3i3.26

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