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Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud

Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud

Rajendra Kumar Dwivedi, Rakesh Kumar, Rajkumar Buyya
Copyright: © 2021 |Volume: 11 |Issue: 1 |Pages: 21
ISSN: 2156-1834|EISSN: 2156-1826|EISBN13: 9781799862444|DOI: 10.4018/IJCAC.2021010103
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MLA

Dwivedi, Rajendra Kumar, et al. "Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud." IJCAC vol.11, no.1 2021: pp.52-72. http://doi.org/10.4018/IJCAC.2021010103

APA

Dwivedi, R. K., Kumar, R., & Buyya, R. (2021). Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud. International Journal of Cloud Applications and Computing (IJCAC), 11(1), 52-72. http://doi.org/10.4018/IJCAC.2021010103

Chicago

Dwivedi, Rajendra Kumar, Rakesh Kumar, and Rajkumar Buyya. "Gaussian Distribution-Based Machine Learning Scheme for Anomaly Detection in Healthcare Sensor Cloud," International Journal of Cloud Applications and Computing (IJCAC) 11, no.1: 52-72. http://doi.org/10.4018/IJCAC.2021010103

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Abstract

Smart information systems are based on sensors that generate a huge amount of data. This data can be stored in cloud for further processing and efficient utilization. Anomalous data might be present within the sensor data due to various reasons (e.g., malicious activities by intruders, low quality sensors, and node deployment in harsh environments). Anomaly detection is crucial in some applications such as healthcare monitoring systems, forest fire information systems, and other internet of things (IoT) systems. This paper proposes a Gaussian distribution-based supervised machine learning scheme of anomaly detection (GDA) for healthcare monitoring sensor cloud, which is an integration of various body sensors of different patients and cloud. This work is implemented in Python. Use of Gaussian statistical model in the proposed scheme improves precision, throughput, and efficiency. GDA provides 98% efficiency with 3% and 4% improvements as compared to the other supervised learning-based anomaly detection schemes (e.g., support vector machine [SVM] and self-organizing map [SOM], respectively).

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