Enhancing Cybersecurity in Medical Cyber-Physical Systems Using Blockchain and Deep Learning

Abstract

Medical Cyber-Physical Systems (MCPS) are essential in modern healthcare, enabling real-time patient monitoring, diagnosis, and treatment. However, their integration of digital and physical elements exposes them to cyber-attacks, threatening both patient safety and data integrity. This paper shows a novel cybersecurity framework that combines blockchain technology and deep learning-based intrusion detection to protect MCPS. The proposed model uses a deep DNN-based intrusion detection system (IDS) to categorize medical data as either normal or malicious. Blockchain technology is incorporated to ensure secure, tamper-proof, and decentralized storage of patient information. To improve detection accuracy and reduce false positives, feature fusion techniques, processed through convolutional neural networks (CNNs), are applied. Ethereum-based blockchain ensures that medical data remains confidential and resistant to unauthorized alterations. The system’s effectiveness is validated using a healthcare dataset, showing a marked improvement in detection accuracy, precision, and recall over traditional methods. This integrated solution strengthens the safety and reliability of MCPS, protecting patient data while maintaining efficient data shared between medical devices and healthcare networks. Future research will focus on real-time applications and scaling the model for larger healthcare systems.