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Related Experiment Videos

Enhancing healthcare information security through VAE-driven anomaly detection in EHR access patterns.

Touseef Iqbal1, Ifrah Raoof2, Mohannad Alkanan3

  • 1UPES Bidholi, School of Computer Science, Dehradun, 248007, India. Touseef.iqbal@ddn.upes.ac.in.

Scientific Reports
|May 26, 2026
PubMed
Summary

This study introduces a privacy-preserving anomaly detection model using Variational Autoencoders (VAEs) to identify suspicious access patterns in Electronic Health Records (EHRs), enhancing healthcare cybersecurity.

Keywords:
Anomaly detectionData augmentationElectronic health records (EHRs)Healthcare information securityInsider threat detectionPrivacy-preserving machine learningVariational autoencoder (VAE)

Related Experiment Videos

Area of Science:

  • Computer Science
  • Healthcare Informatics
  • Cybersecurity

Background:

  • Digitalization of healthcare increases risks of HIPAA breaches and unauthorized access to Electronic Health Records (EHRs).
  • Effective security models are challenged by limited access to real-world healthcare data due to privacy constraints.

Purpose of the Study:

  • To propose a privacy-preserving anomaly detection model for identifying suspicious access patterns in EHR systems.
  • To address the challenge of limited real-world data by utilizing a synthetically enriched EHR access log dataset.

Main Methods:

  • Developed a Variational Autoencoder (VAE) based anomaly detection model.
  • Generated a synthetic EHR access log dataset with realistic features (e.g., user roles, timestamps).
  • Evaluated the VAE model against traditional machine learning algorithms like Isolation Forest and One-Class SVM.

Main Results:

  • The VAE model demonstrated superior performance in detecting anomalies, achieving an F1-score of 0.93.
  • Achieved lower false-positive rates and greater sensitivity to noisy data compared to traditional methods.
  • The model effectively identified deviations in access patterns without accessing sensitive patient data.

Conclusions:

  • Unsupervised deep generative modeling combined with synthetic data generation offers a novel, privacy-conserving approach to enhance medical information system cybersecurity.
  • VAE-based anomaly detection is a promising solution for protecting healthcare infrastructure against evolving cyber risks.