AUTHOR=Shaikh Jamshed Ali , Wang Chengliang , Muhammad Wajeeh Us Sima , Arshad Muhammad , Owais Muhammad , Alnashwan Rana Othman , Chelloug Samia Allaoua , Muthanna Mohammed Saleh Ali TITLE=RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system JOURNAL=Frontiers in Digital Health VOLUME=Volume 6 - 2024 YEAR=2024 URL=https://www.frontiersin.org/journals/digital-health/articles/10.3389/fdgth.2024.1467241 DOI=10.3389/fdgth.2024.1467241 ISSN=2673-253X ABSTRACT=The Internet of Medical Things (IoMT) has revolutionized healthcare with remote patient monitoring and real-time diagnosis, but securing patient data remains a critical challenge due to sophisticated cyber threats and the sensitivity of medical information. Traditional machine learning methods struggle to capture the complex patterns in IoMT data, and conventional intrusion detection systems often fail to identify unknown attacks, leading to high false positive rates and compromised patient data security. To address these issues, we propose RCLNet, an effective Anomaly-based Intrusion Detection System (A-IDS) for IoMT. RCLNet employs a multi-faceted approach, including Random Forest (RF) for feature selection, the integration of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) models to enhance pattern recognition, and a Self-Adaptive Attention Layer Mechanism (SAALM) designed specifically for the unique challenges of IoMT. Additionally, RCLNet utilizes focal loss (FL) to manage imbalanced data distributions, a common challenge in IoMT datasets. Evaluation using the WUSTL-EHMS-2020 healthcare dataset demonstrates that RCLNet outperforms recent state-of-the-art methods, achieving a remarkable accuracy of 99.78%, highlighting its potential to significantly improve the security and confidentiality of patient data in IoMT healthcare systems.