Development of Robust Trust Management Framework for Medical IoT

Abstract: Modern healthcare without IoT devices is un imaginable. They've diffused into almost every corner of patient monitoring—tracking heart rates, oxygen levels, you name it. And honestly, things run a lot smoother because of them. But this shift hasn't come without problems—security and trust have become major concerns. To tackle this, we built a system that brings together blockchain and deep learning to keep an eye on devices in real time and figure out which ones can be trusted. The system uses three different deep learning models working together to constantly evaluate device trust. One looks for anomalies using LSTM, another analyzes behavior with CNN, and a third predicts threats through GRU. All of this is backed by a simple but effective blockchain that keeps a tamper-proof record of trust scores and transactions—making things transparent and easy to audit Healthcare administrators can monitor device status, see trust distributions, handle alerts, and investigate blockchain transactions using the system's interactive graphical user interface, which was created with Tkinter. Effective trust score computation across various device types, including the heart, is demonstrated by experimental results

Keywords: blockchain, deep learning, healthcare security, Internet of Things, trust management