A Hybrid Autoencoder-Random Forest Framework for Intrusion Detection in Internet of Medical Things Network
TL;DR
Results demonstrate the efficacy of strategic dimensionality reduction in securing critical healthcare infrastructures and integrating the SHAP algorithm ensures decision-making transparency, addressing the “black box" challenge in medical AI.
Abstract
With the widespread adoption of the Internet of Medical Things (IoMT), hospitals have become prime targets for cyberattacks. To overcome the limitations of traditional defenses and computationally heavy deep learning models, a hybrid Artificial Intelligence architecture is presented. By coupling a deep Autoencoder for feature extraction with a Random Forest classifier, the input space is reduced from 44 down to 16 latent dimensions. While a standalone Random Forest achieves 98.95%accuracy, it remains too resource-intensive for constrained edge devices. The proposed hybrid approach strategically accepts a marginal sacrifice in accuracy—achieving 94.60% overall—in exchange for a drastic reduction in computational complexity. Rigorously validated on the CICIoMT2024 dataset and balanced via SMOTE-ENN, this model achieves an ultra-low inference latency of 1.05 ms on edge-grade hardware, making it highly viable for Edge Computing deployment. Furthermore, integrating the SHAP algorithm ensures decision-making transparency, addressing the “black box" challenge in medical AI. These results demonstrate the efficacy of strategic dimensionality reduction in securing critical healthcare infrastructures.