An Intelligent Intrusion Detection and Privacy-Preserving Architecture for the Internet of Medical Things (IoMT)
TL;DR
This work proposes an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead, and proposes an adaptive FedProx-based weighted federated learning framework.
Abstract
The Internet of Medical Things (IoMT) is transforming healthcare delivery, but brings significant security and privacy challenges due to the diverse range of devices, sensitive patient data, and real-time operation requirements. Existing Intrusion Detection Systems (IDS) have improved detection and privacy through approaches such as federated learning and blockchain, yet they focus primarily on network-level attacks, overlooking device-level attacks which is the primary source of data leakage, device unavailability, and model poisoning in federated learning (FL)-based approaches. For instance, Bring Your Own Device (BYOD) introduces heterogeneity and Non-Independent and Identically Distributed data (non-IID) distributions that affect the performance of conventional FL approaches. We therefore propose an intelligent, lightweight Tiny LSTM–GRU hybrid IDS on the edge to monitor device-generated behavioral patterns in real time, with minimal computational and energy overhead. To preserve privacy and handle the issue of non-IID data across heterogeneous IoMT devices, we propose an adaptive FedProx-based weighted federated learning framework. Our proposed framework achieves an overall accuracy of 99.1% on the edge with latency between 1.8ms per sample, with a mean global accuracy of 99.4% and global loss of 0.037 during convergence, making it highly suitable for real-world IoMT deployments.