A comparative study of cyber security intrusion detection in healthcare systems

Abstract

Due to the proliferation of network devices and the presence of sensitive information, healthcare systems have become prime targets for cyber attackers. Therefore, it is crucial to design an efficient and accurate intrusion detection system (IDS) specifically tailored for healthcare systems. In this regard, we conducted a comprehensive comparative study on network security intrusion detection in healthcare systems. In order to tackle the challenges arising from information redundancy and noise in feature selection, we developed the Maximum Information Coefficient (MIC) method to effectively analyse the nonlinear relationships among traffic features. This method was utilized in a comparative analysis involving ten models on three datasets. The experiments demonstrated that the detection models using MIC-based feature selection outperformed other feature selection approaches, especially when applied to the WUSTL-EHMS-2020 dataset, which includes patients' biometric features. The MIC-enhanced Extreme Gradient Boosting detection model achieved remarkable results, attaining an accuracy of 95.01%, precision of 94.94%, and recall of 95.01%. These findings underscore the efficacy of our comparative study in safeguarding healthcare systems against cyber attacks. Furthermore, our study highlights the importance of feature selection and the incorporation of patient biometric features in healthcare IDS. It is imperative for medical managers to consider these factors when making informed decisions regarding cyber security measures.