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Improving internet of health things security through anomaly detection framework using artificial intelligence driven

Manal Abdullah Alohali1, Mohammad Alamgeer2, Ali M Al-Sharafi3

  • 1Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

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Summary

This study introduces an AI-driven method to detect cyberattacks in the Internet of Health Things (IoHT), achieving 99.33% accuracy. The Enhancing Internet of Health Things Security through Cyberattack Detection Using Serial Exponential Golf Optimization (EIoHTSCD-SEGO) technique enhances healthcare cybersecurity.

Keywords:
Anomaly detectionBidirectional long short-term memoryData scienceEnsemble of deep learningInternet of health thingsSerial exponential golf optimization

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Area of Science:

  • Cybersecurity
  • Health Informatics
  • Artificial Intelligence

Background:

  • Healthcare systems face increasing cybersecurity threats due to widespread technology adoption.
  • Internet of Health Things (IoHT) devices are vulnerable to cyberattacks, necessitating robust detection methods.
  • Machine learning (ML) and Artificial Intelligence (AI) offer advanced capabilities for anomaly detection in cybersecurity.

Purpose of the Study:

  • To develop an AI-driven technique for detecting cyberattacks in the Internet of Health Things (IoHT) environment.
  • To enhance the security of IoHT by accurately classifying anomalous patterns indicative of cyber threats.
  • To improve the efficiency and accuracy of cyberattack detection in healthcare settings.

Main Methods:

  • The Enhancing Internet of Health Things Security through Cyberattack Detection Using Serial Exponential Golf Optimization (EIoHTSCD-SEGO) technique was developed.
  • Data preprocessing involved TF-IDF for feature vectors and min-max normalization.
  • An ensemble of deep learning (DL) classifiers (RNN, BiLSTM, KELM) was optimized using the Serial Exponential Golf Optimization Algorithm (SEGOA).

Main Results:

  • The EIoHTSCD-SEGO technique achieved a superior accuracy of 99.33% in cyberattack detection.
  • The method demonstrated effective classification of anomaly detection using AI-based data science.
  • Performance validation was conducted using the ECU-IoHT benchmark dataset.

Conclusions:

  • The developed EIoHTSCD-SEGO technique significantly improves cybersecurity in IoHT environments.
  • AI-driven anomaly detection, particularly with DL ensembles and optimization algorithms, is highly effective for healthcare cybersecurity.
  • The study highlights the potential of advanced ML/AI techniques to mitigate cyber risks in connected healthcare systems.