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Dynamic Data Infrastructure Security for Interoperable e-Healthcare Systems: A Semantic Feature-Driven NoSQL

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A novel model effectively detects NoSQL intrusion attacks in the Internet of Medical Things (IoMT) e-Healthcare systems. It achieves high accuracy using semantic features, feature selection, and a random forest classifier for robust database security.

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Health Informatics

Background:

  • The Internet of Medical Things (IoMT) and interoperable e-Healthcare systems face significant database security challenges due to their heterogeneous and dynamic nature.
  • Classical databases are vulnerable to intrusion attacks like bot-attacks and malware, and even NoSQL databases struggle with dynamic data in complex environments.
  • Ensuring the security of electronic healthcare records (EHRs) and telemedicine platforms is critical for patient data protection.

Purpose of the Study:

  • To propose a novel semantic feature-driven NoSQL intrusion attack (NoSQL-IA) detection model specifically for interoperable e-Healthcare systems.
  • To evaluate various semantic feature extraction, feature selection, and resampling techniques for optimizing intrusion detection.
  • To identify the most effective machine learning classifier for accurately identifying NoSQL-IA in e-Healthcare databases.

Main Methods:

  • Assessed semantic feature extraction methods (Word2Vec, CBOW, SKG, Count Vectorizer, TF-IDF, GLOVE) for NoSQL-IA prediction.
  • Employed feature selection algorithms (WRST, Select K-Best, PCA, VTFS) to reduce computational load and improve efficiency.
  • Utilized resampling techniques (upsampling, downsampling, SMOTE) to address data imbalance, followed by Min-Max normalization and evaluation with multiple ML classifiers (Random Forest, XGBoost, etc.).

Main Results:

  • The combination of Word2Vec features with N-Skip Gram (SKG), Variance Threshold Feature Selection (VTFS), SMOTE resampling, and a bootstrapped Random Forest classifier yielded the highest performance.
  • Achieved exceptional results with 98.86% accuracy, 0.974 F-Measure, and 0.981 Area Under the Curve (AUC).
  • This integrated approach demonstrated superior capability in distinguishing regular queries from NoSQL-IA attack queries.

Conclusions:

  • The proposed semantic feature-driven NoSQL-IA detection model is highly effective for securing interoperable e-Healthcare databases.
  • The optimal configuration (Word2Vec-SKG, VTFS, SMOTE, Random Forest) provides a robust solution against sophisticated intrusion attacks.
  • This research contributes a significant advancement in protecting sensitive health data within the evolving IoMT landscape.