1 research outputs found
Detecting Anomalous User Behavior in Remote Patient Monitoring
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