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Summary
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A new Intrusion Detection System (IDS) enhances Internet of Medical Things (IoMT) security by using a honeypot and an ensemble model. This robust system effectively detects and classifies cyberattacks on IoMT networks, safeguarding sensitive patient data.

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Artificial Intelligence (AI)Deep Learning (DL)HoneypotInternet of Medical Things (IoMT)Intrusion Detection System (IDS)Machine Learning (ML)ensemble methodgood health and well-being

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Area of Science:

  • Cybersecurity
  • Health Informatics
  • Network Security

Background:

  • The integration of the Internet of Medical Things (IoMT) in healthcare enables efficient data analysis for improved patient diagnosis.
  • However, IoMT networks face significant security risks, including data breaches and network traffic interception, compromising patient confidentiality.
  • Existing security measures are insufficient to address the evolving threat landscape in IoMT environments.

Purpose of the Study:

  • To develop and evaluate a robust Intrusion Detection System (IDS) specifically designed for Internet of Medical Things (IoMT) networks.
  • To enhance the security and privacy of sensitive patient data transmitted within IoMT ecosystems.
  • To improve the accuracy and reliability of detecting various cyberattacks targeting IoMT infrastructure.

Main Methods:

  • Implementation of a novel IDS incorporating a honeypot to decoy attackers and reduce the attack surface.
  • Utilization of an ensemble machine learning approach, combining Logistic Regression and K-Nearest Neighbor algorithms for enhanced detection capabilities.
  • Performance evaluation using two distinct IoMT datasets featuring diverse attack types, including Man-In-The-Middle (MITM), Data Injection, and Distributed Denial of Services (DDOS).

Main Results:

  • The proposed ensemble IDS demonstrated high effectiveness in identifying and classifying malicious activities within IoMT networks.
  • Achieved a high accuracy rate of 92.5% on the first dataset and 99.54% on the second dataset.
  • Attained a precision of 96.74% for the first dataset and 99.228% for the second dataset, indicating reliable attack detection.

Conclusions:

  • The developed IDS, leveraging a honeypot and an ensemble model, provides a robust solution for securing IoMT networks.
  • The ensemble approach significantly improves the accuracy and precision of intrusion detection in healthcare environments.
  • This research contributes to safeguarding sensitive patient data and ensuring the integrity of IoMT systems against sophisticated cyber threats.