Detecting Anomalous User Behavior in Remote Patient Monitoring

TL;DR

This paper introduces an HMM-based anomaly detection framework for Remote Patient Monitoring that effectively identifies abnormal user behaviors with over 98% accuracy, enhancing security in IoMT healthcare systems.

Contribution

The paper presents a novel HMM-based model for detecting anomalies in IoMT and smart home data, improving security in remote healthcare monitoring.

Findings

Achieved over 98% accuracy in anomaly detection.

Developed a testbed with IoMT devices and sensors for data collection.

Demonstrated effectiveness of HMM in real-world healthcare scenarios.

Abstract

The growth in Remote Patient Monitoring (RPM) services using wearable and non-wearable Internet of Medical Things (IoMT) promises to improve the quality of diagnosis and facilitate timely treatment for a gamut of medical conditions. At the same time, the proliferation of IoMT devices increases the potential for malicious activities that can lead to catastrophic results including theft of personal information, data breach, and compromised medical devices, putting human lives at risk. IoMT devices generate tremendous amount of data that reflect user behavior patterns including both personal and day-to-day social activities along with daily routine health monitoring. In this context, there are possibilities of anomalies generated due to various reasons including unexpected user behavior, faulty sensor, or abnormal values from malicious/compromised devices. To address this problem, there is an imminent need to develop a framework for securing the smart health care infrastructure to identify and mitigate anomalies. In this paper, we present an anomaly detection model for RPM utilizing IoMT and smart home devices. We propose Hidden Markov Model (HMM) based anomaly detection that analyzes normal user behavior in the context of RPM comprising both smart home and smart health devices, and identifies anomalous user behavior. We design a testbed with multiple IoMT devices and home sensors to collect data and use the HMM model to train using network and user behavioral data. Proposed HMM based anomaly detection model achieved over 98% accuracy in identifying the anomalies in the context of RPM.