Towards Secure Healthcare: Intrusion Detection with GraphSAGE-Based Meta-Learning and Hybrid Deep Models

Outlet Title

2025 Cyber Awareness and Research Symposium (CARS)

Document Type

Conference Proceeding

Publication Date

Winter 1-21-2026

Abstract

The Internet of Medical Things (IoMT) has transformed modern healthcare by enabling intelligent, real-time monitoring, and decision making. However, its reliance on open communication networks has introduced significant security and privacy risks. This study presents analysis of intrusion detection algorithms designed specifically for IoMT environments. The first is a graph-based intrusion detection system implemented on the GraphSAGE framework (EGraphSAGE), to improve the detection of both familiar and previously unseen attacks. The second approach is a blended deep learning framework that combines Convolutional Neural Networks (CNN) and Long- and Short-Term Memory (LSTM) models to efficiently identify spatial and temporal intrusion patterns in edge-centric environments. Both systems are evaluated using publicly available intrusion detection datasets, specifically the IoMT attacks and ACI-IoT-2023 datasets. Experimental results indicate that the EGraphSAGE model achieves competitive performance in terms of detection accuracy and misclassification rate on a smaller dataset. On the other hand, the CNN+LSTM framework offers a lightweight deployment with strong temporal feature extraction in any dataset. The findings offer valuable insight into the strengths and limitations of both approaches, paving the way for improved adaptive intrusion detection solutions in healthcare.

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