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

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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…

Citation impact

282
total citations
FWCI
87.14
Percentile
100%
References
31
Citations per year

Authors

7

Topics & keywords

Keywords
  • Anomaly detection
  • Cloud computing
  • Computer science
  • Computer security
  • Artificial intelligence
  • Operating system
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