Detecting Anomalous User Behavior in Remote Patient Monitoring

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.

Hierarchical Federated Learning based Anomaly Detection using Digital Twins for Smart Healthcare

“…These models have used various ML approaches to classify the data into normal/abnormal categories. Gupta et al [17] introduced a centralized AD model for a single user in the RPM ecosystem. However, as discussed earlier, centralized AD models are facing privacy, high latency and high communication cost issues.…”

“…In the past, various Anomaly Detection (AD) models [6], [10], [28], [35] have been developed to secure RPM ecosystem based on patients' behavior. Gupta et al [17] proposed Hidden Markov Model (HMM) based AD for RPM by using smart home and smart health devices that analyzes anomalous users' behavior. More recently the US National Institute of Standards and Technology (NIST) published a report [8] on the RPM ecosystem, which highlights possible security and privacy solutions to build a secure RPM infrastructure.…”