A Human-Centered Quantum Machine Learning Framework for Attack Detection in IoT-Based Healthcare Industry 5.0

Industry 5.0 aims to transform the healthcare sector by integrating emerging technologies like Artificial Intelligence (AI) and the Internet of Things (IoT) with a human-centered focus on patient wellness and preventive care. Although this approach promises personalized care and improved sustainability in healthcare systems, it also introduces cyber risks that could lead to economic and physical losses. This emphasizes the urgent need for enhanced cybersecurity measures in healthcare Industry 5.0 systems. Therefore, this paper presents a new framework for detecting cyberattacks while protecting patient data. The framework employs a human-centric approach and Quantum Random Forest (QRF) with local differential privacy for effective attack detection. It also integrates active learning with threat intelligence feeds and generative AI tools like ChatGPT to further support human roles and improve detection capabilities. We evaluated the efficiency of our proposed framework in terms of performance metrics such as accuracy, detection rate, time, and memory complexity. The experimental results show that our proposed framework excelled in attack detection using the ICU and WUST-EHMS-2020 datasets.