Stacking ensemble learning with heterogeneous models and selected feature subset for prediction of service trust in internet of medical things

Abstract

Recently, with the fast development of IoT, Internet of medical things (IoMT) has drawn wide attention from both industry and academia. However, pressing challenges exist in practical implementation of IoMT, such as service provision with stringent latency. To address the challenges, fog computing is generally employed in IoMT systems. However, it raises additional concerns of trust and security. To tackle the issue, the authors introduce the security measure of trust into this work, and a superior heterogeneous stacking ensemble learning measure for trustworthiness prediction (SEM‐TP) of fog services is proposed. Besides, to reduce unnecessary time cost incurred by unimportant features, an efficient voting‐based feature selection (FS) strategy called voting‐based feature selection method is proposed to select significant features, which is based on diverse FS measures. Extensive experiments are conducted and the results show that the proposed framework outperforms commonly used single classifiers and competing stacking models in terms of Accuracy, Precision, Recall, F1‐score, Kappa coefficient, and Hamming distance under different conditions, validating the effectiveness, robustness, and superiority of the proposed trustworthiness prediction and FS methods.

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

“…In this context, the application of ensemble learning in intrusion detection systems (IDS) for IoT and IoMT networks presents a promising approach to enhance cybersecurity measures. Researchers have explored various ensemble techniques, such as stacking, bagging, and boosting, to optimize the combination of diverse base models for improved predictive accuracy and robustness in detecting anomalies within IoT and IoMT environments [ 2 , 3 ]. The integration of ensemble learning in IDS for IoT and IoMT networks addresses the critical need for reliable security solutions to protect sensitive medical data and ensure the integrity of healthcare systems amidst the proliferation of connected devices and services [ 4 , 5 ].…”

Wireless body area networks task offloading method combined with multiple communication and computing resources supported by MEC

“…Some elderly people, especially those with chronic diseases, urgently need real-time monitoring by doctors to ensure the safety and health of their lives. In recent years, with the advancement of Internet of Things (IoT) technology [1,2], the rapid development of new fields such as smart healthcare, smart city and smart grid has been promoted [3]. As the typical application in the field of smart healthcare, wireless body area networks (WBANs) [4,5] have attracted the attention of various sectors of the society, including academia and industry, and have become a feasible solution for real-time monitoring of patients' physical health.…”