Integrating Big Data and Machine Learning for Effective Cyberattack Prediction in e-Health Information Systems

This study proposes an intrusion-prediction framework for e-Health information systems that combines structured web-log analysis, supervised machine learning, and Apache Spark-based distributed processing. A corpus of 1,000,000 labeled HTTP log instances collected from a university hospital web environment was preprocessed into security-relevant features, including request method, request/response type, packet size, status code, URL length, and parameter count. Using a stratified 80/20 train-test split and five-fold cross-validation on the training data, we compared K-Nearest Neighbors (KNN), Logistic Regression, and Decision Trees. KNN achieved the best held-out performance, with 95.66% accuracy, 91.79% precision, 93.93% recall, 92.85% F1-score, and a 3.60% false positive rate. Logistic Regression and Decision Trees reached accuracies of 85.30% and 83.20%, respectively. Spark also reduced runtime substantially at the 1,000,000-instance scale, lowering KNN processing time from 12.0 s to 6.5 s. The results show that combining big data infrastructure with carefully tuned machine learning can improve both detection quality and operational feasibility in hospital cybersecurity monitoring.

@article{Abdelbaki2026,
  title     = {Integrating Big Data and Machine Learning for Effective Cyberattack Prediction in e-Health Information Systems},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {4},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Abdelbaki and Latif Adnane and Charaf Eddine Ait Zaouiat},
  doi       = {10.14569/IJACSA.2026.0170446},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170446}
}