RCLNet: an effective anomaly-based intrusion detection for securing the IoMT system

Wang Chengliang

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