Citation: Albattah, A.; Rassam, M.A.
A Correlation-Based Anomaly
Detection Model for Wireless Body
Area Networks Using Convolutional
Long Short-Term Memory Neural
Network. Sensors 2022, 22, 1951.
https://doi.org/10.3390/s22051951
Academic Editors: Alvaro Araujo
Pinto and Hacene Fouchal
Received: 6 January 2022
Accepted: 25 February 2022
Published: 2 March 2022
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Article
A Correlation-Based Anomaly Detection Model for Wireless
Body Area Networks Using Convolutional Long Short-Term
Memory Neural Network
Albatul Albattah
1
and Murad A. Rassam
1,2,
*
1
Department of Information Technology, College of Computer, Qassim University,
Buraidah 52571, Saudi Arabia; 411207333@qu.edu.sa
2
Faculty of Engineering and Information Technology, Taiz University, Taiz 6803, Yemen
* Correspondence: m.qasem@qu.edu.sa
Abstract:
As the Internet of Healthcare Things (IoHT) concept emerges today, Wireless Body Area
Networks (WBAN) constitute one of the most prominent technologies for improving healthcare
services. WBANs are made up of tiny devices that can effectively enhance patient quality of life
by collecting and monitoring physiological data and sending it to healthcare givers to assess the
criticality of a patient and act accordingly. The collected data must be reliable and correct, and
represent the real context to facilitate right and prompt decisions by healthcare personnel. Anomaly
detection becomes a field of interest to ensure the reliability of collected data by detecting malicious
data patterns that result due to various reasons such as sensor faults, error readings and possible
malicious activities. Various anomaly detection solutions have been proposed for WBAN. However,
existing detection approaches, which are mostly based on statistical and machine learning techniques,
become ineffective in dealing with big data streams and novel context anomalous patterns in WBAN.
Therefore, this paper proposed a model that employs the correlations that exist in the different
physiological data attributes with the ability of the hybrid Convolutional Long Short-Term Memory
(ConvLSTM) techniques to detect both simple point anomalies as well as contextual anomalies in the
big data stream of WBAN. Experimental evaluations revealed that an average of 98% of F1-measure
and 99% accuracy were reported by the proposed model on different subjects of the datasets compared
to 64% achieved by both CNN and LSTM separately.
Keywords:
anomaly detection; wireless body area networks; spatiotemporal correlation;
convolutional
neural networks; long short-term memory; deep learning
1. Introduction
The accelerated development of the Internet of Things (IoT) has attracted attention
from stakeholders all over the world due to the combination of the physical world with
the virtual world through the Internet for communication and data sharing. IoT has been
defined as an interrelated system of mechanical and digital machines, computing devices
and objects that is capable of transmitting data over a network without involving human-
to-human or human-to-machine interaction. IoT becomes more prevalent every day in
many life aspects such as industrial sectors, financial sectors, and healthcare sectors [1].
In healthcare, IoT has improved the quality of care provided to patients. Indeed, people
can lead more comfortable lives as it guarantees their health and safety through continuity
monitoring. In addition, it supports a wide range of applications, from implantable medical
implants to Wireless Body Area Networks (WBAN). WBAN is composed of tiny devices
that are considered the most promising technologies for improving healthcare services.
These devices have enabled remote monitoring to enhance the overall quality of care
provided to patients in remote areas or medical facilities [2,3].
Sensors 2022, 22, 1951. https://doi.org/10.3390/s22051951 https://www.mdpi.com/journal/sensors
