TY  - EJOU
AU  - Usama, Muhammad 
AU  - Aziz, Arshad 
AU  - Hassan, Imtiaz 
AU  - Akhmetzhanova, Shynar 
AU  - Qasem, Sultan Noman 
AU  - Albarrak, Abdullah M. 
AU  - Al-Hadhrami, Tawfik 

TI  - Enhancing Healthcare Cybersecurity through the Development and Evaluation of Intrusion Detection Systems
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2025
VL  - 144
IS  - 1
SN  - 1526-1506

AB  - The increasing reliance on digital infrastructure in modern healthcare systems has introduced significant cybersecurity challenges, particularly in safeguarding sensitive patient data and maintaining the integrity of medical services. As healthcare becomes more data-driven, cyberattacks targeting these systems continue to rise, necessitating the development of robust, domain-adapted Intrusion Detection Systems (IDS). However, current IDS solutions often lack access to domain-specific datasets that reflect realistic threat scenarios in healthcare. To address this gap, this study introduces HCKDDCUP, a synthetic dataset modeled on the widely used KDDCUP benchmark, augmented with healthcare-relevant attributes such as patient data, treatments, and diagnoses to better simulate the unique conditions of clinical environments. This research applies standard machine learning algorithms Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) to both the KDDCUP and HCKDDCUP datasets. The methodology includes data preprocessing, feature selection, dimensionality reduction, and comparative performance evaluation. Experimental results show that the RF model performed best, achieving 98% accuracy on KDDCUP and 99% on HCKDDCUP, highlighting its effectiveness in detecting cyber intrusions within a healthcare-specific context. This work contributes a valuable resource for future research and underscores the need for IDS development tailored to sector-specific requirements.
KW  - Cybersecurity; KDDCUP; HCKDDCUP; machine learning; anomaly detection; data privacy

DO  - 10.32604/cmes.2025.067098