Quantum Cryptography and Machine Learning Integration for Secure Communication in Distributed Healthcare Systems

Abstract

Distributed healthcare, cloud-enabled medical services, Internet of Medical Things (IoMT)-based technologies have greatly enhanced access to healthcare and real-time monitoring of patients, but the same technologies have created significant cybersecurity problems associated with unauthorized access, ransomware attacks, data interception, and sensitive patient information breaches. Secure communication in a distributed healthcare setting has become a substantial need, therefore, as conventional cryptography approaches are progressively becoming susceptible to novel and sophisticated cybercrimes as well as novel quantum computing assaults. The study presents a secure communication model, combining Quantum Key Distribution (QKD) based on the BB84 protocol and machine learning-based adaptive intrusion detection to distributed healthcare systems. The given framework uses BB84 quantum cryptography to create safe key exchange and encrypted healthcare communication networks, whereas the machine learning module would detect and classify malicious cyberattacks in real-time such as denial-of-service attacks, unauthorized access, malware, and network anomalies. The efficiency of the developed framework in the case of various attacks was evaluated experimentally on standard datasets on cybersecurity like NSL-KDD and CICIDS2017. Analysis of performance was conducted based on classification measures such as Accuracy, Precision, Recall, and F1-score as well as an assessment of attack vulnerability reduction and enhanced security in communication. As the results of the experiment show, the offered BB84 and machine learning-enhanced framework has a much greater intrusion detection capacity, leads to better secure healthcare communication, lower vulnerability to cyberattacks, and has a more solid defense against each eavesdropping and unauthorized intrusion than the use of traditional security mechanisms. The presented framework is a step towards scalable and intelligent healthcare cybersecurity systems, as it fuses quantum-safe communication with adaptive machine learning-based threat detection, as well as emphasizes the future potential of quantum artificial intelligence-based security architectures in the future of distributed healthcare infrastructure.