Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview

Document Type : Original Article

Authors

Peyman Vafadoost Sabzevar ¹

Hamidreza Rokhsati ²

Alireza Chamansara ³

Ahmad Hajipour ⁴

¹ Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran.

² Department of Computer, Control and Management Engineering, Sapienza University, Rome, Italy.

³ Department of Biomedical Engineering, Materials and Energy Research Center, Tehran, Iran.

⁴ Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran.

10.22067/cke.2024.89572.1127

Abstract

In today's era, the Internet of Things has become one of the important pillars in organizations, hospitals, and research circles and is recognized as an integral part of the Internet. One of the important areas that require online monitoring is medical imaging equipment, whose functional information is transmitted through the Internet of Things. Server security and intrusion prevention, along with anomaly detection, are critical requirements for these networks. The purpose of anomaly detection is to develop methods that can detect attackers' attacks and prevent them from happening again. Algorithms and methods based on statistics play an important role in predicting and diagnosing anomalies. In this article, the isolation forest algorithm was used for training on 80% of the dataset related to the data of the Internet of Medical Things network, and then this model was tested and evaluated on the remaining 20%. The results show 90.54% accuracy in detecting anomalies in the received data, which confirms the effective performance of this method in this field.

Keywords

Anomaly

Detection Anomaly

Internet of Medical Things

Isolation Forest

Unsupervised Learning

Subjects

• Machine learning

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