A privacy-aware framework for detecting cyber attacks on internet of medical things systems using data fusion and quantum deep learning

Abstract

Internet of Medical Things (IoMT) devices and systems are often designed without adequate security, leaving them highly susceptible to cyber threats. Unlike other IoT applications and devices, cyber attacks against IoMT devices will pose significant risks to patient safety. As a result of the progress made in attack detection through machine learning, numerous applications of machine learning are now involved in IoMT. However, how to securely collect and fuse the data from heterogeneous IoMT devices and systems and ensure efficient cyber-attack detection while avoiding the privacy disclosure of the dataset remains challenging for IoMT systems. To fill this gap, a new privacy-aware framework is advised for storing the collected IoMT data in distributed cloud nodes and providing data fusion of heterogeneous IoMT data using differential privacy and deep learning and efficient attack detection using quantum deep learning with maintaining data privacy. Specifically, the proposed privacy-aware framework provides sensitive data privacy protection at multi-levels starting from storing data in distributed cloud nodes with privacy-preserving and then fusing IoMT heterogeneous data using deep contractive autoencoder with differential privacy for protecting the sensitive data through the learning process and then using the output of data fusion which is reduced data dimension as input for a quantum neural network for identifying cyber-attacks. Our experimental results indicate that the proposed framework is highly effective in detecting cyber-attacks.