Enhancing Healthcare Cybersecurity through the Development and Evaluation of Intrusion Detection Systems
Usama M. Aziz A. Hassan I. Akhmetzhanova S. Qasem S.N. Albarrak A.M. Al-Hadhrami T.
2025Tech Science Press
CMES - Computer Modeling in Engineering and Sciences
2025#144Issue 11225 - 1248 pp.
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. Copyright
anomaly detection , Cybersecurity , data privacy , HCKDDCUP , KDDCUP , machine learning
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Department of Cyber Security, Pakistan Navy Engineering College, National University of Sciences and Technology, Karachi, 75350, Pakistan
Department of Computer Science, Main Campus, Iqra University, Karachi, 75500, Pakistan
Department of Computer Engineering, Astana IT University, Astana, 010000, Kazakhstan
Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11432, Saudi Arabia
Computer Science Department, School of Science and Technology, Nottingham Trent University, Nottingham, NG11 8NS, United Kingdom
Department of Cyber Security
Department of Computer Science
Department of Computer Engineering
Computer Science Department
Computer Science Department
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