BIoMT-ISeg: Blockchain internet of medical things for intelligent segmentation

Abstract

In the quest of training complicated medical data for Internet of Medical Things (IoMT) scenarios, this study develops an end-to-end intelligent framework that incorporates ensemble learning, genetic algorithms, blockchain technology, and various U-Net based architectures. Genetic algorithms are used to optimize the hyper-parameters of the used architectures. The training process was also protected with the help of blockchain technology. Finally, an ensemble learning system based on voting mechanism was developed to combine local outputs of various segmentation models into a global output. Our method shows that strong performance in a condensed number of epochs may be achieved with a high learning rate and a small batch size. As a result, we are able to perform better than standard solutions for well-known medical databases. In fact, the proposed solution reaches 95% of intersection over the union, compared to the baseline solutions where they are below 80%. Moreover, with the proposed blockchain strategy, the detected attacks reached 76%.

Enhancing the Internet of Medical Things (IoMT) Security with Meta-Learning: A Performance-Driven Approach for Ensemble Intrusion Detection Systems

“…The use of ensemble learning in IDS for IoT and IoMT networks provides a sophisticated approach to combat cyber threats and enhance network security. By utilizing meta-learning and ensemble techniques, researchers can develop adaptive intrusion detection mechanisms that dynamically adjust decision fusion strategies based on evolving attack patterns and network conditions [ 6 , 7 ]. The combination of machine learning algorithms, deep learning models, and ensemble methods in IDS for IoMT networks not only improves anomaly detection capabilities but also contributes to the creation of lightweight and efficient security solutions tailored to the unique challenges posed by interconnected medical devices and IoT ecosystems [ 8 , 9 ].…”