Collaborative Intelligence for Securing Next-Generation Healthcare Systems Against Cyber Risks

Abstract

With the rapid integration of modern technologies and biotechnologies, next-generation healthcare environments are becoming increasingly dependent on interconnected smart devices. The Industry 5.0 healthcare paradigm focuses on hyper-personalization, aiming to provide human-centric, adaptive healthcare solutions through the fusion of the Internet of Things (IoT), the Internet of Medical Things (IoMT), and Artificial Intelligence (AI). This advanced paradigm allows tailored medical care for patients with diverse health conditions, improving diagnostic accuracy, treatment efficiency, and overall patient outcomes. However, with this shift toward intelligent, data-driven infrastructure comes a significant rise in cybersecurity concerns, particularly the growing vulnerability to sophisticated cyber threats targeting healthcare systems. To address these challenges, a collaborative intelligence-based intrusion detection approach has been proposed, leveraging ensemble learning techniques for real-time detection and prevention of cyber-attacks. The method utilizes the NSL-KDD dataset, a benchmark dataset for evaluating intrusion detection systems, to validate performance across multiple classifiers. The technique evaluates key machine learning algorithms, including k-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Tree, and introduces a robust Stacking Classifier that integrates the strengths of Random Forest and Light Gradient Boosting Machine (LightGBM). These algorithms are assessed based on critical performance metrics such as accuracy, precision, recall, and F1-score. Experimental results reveal that the ensemble-based Stacking Classifier achieves 100% accuracy, outperforming individual classifiers and showcasing the potential of combined models in detecting anomalous network behavior effectively. This demonstrates the importance of collaborative intelligence in forming a resilient cybersecurity layer for smart healthcare applications. Such a security mechanism is vital for safeguarding sensitive medical data and maintaining trust in intelligent, automated, and highly personalized healthcare delivery systems in the Industry 5.0 era.