Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview
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 inform...
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| Format: | Article |
| Language: | English |
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Ferdowsi University of Mashhad
2024-12-01
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| Series: | Computer and Knowledge Engineering |
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| Online Access: | https://cke.um.ac.ir/article_45899_d051a3bf558ccf20a5b47e541d03d925.pdf |
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| _version_ | 1832595476574109696 |
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| author | Peyman Vafadoost Sabzevar Hamidreza Rokhsati Alireza Chamansara Ahmad Hajipour |
| author_facet | Peyman Vafadoost Sabzevar Hamidreza Rokhsati Alireza Chamansara Ahmad Hajipour |
| author_sort | Peyman Vafadoost Sabzevar |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-753b3a8307964e0da4a7ff2f36d9ec30 |
| institution | Kabale University |
| issn | 2538-5453 2717-4123 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ferdowsi University of Mashhad |
| record_format | Article |
| series | Computer and Knowledge Engineering |
| spelling | doaj-art-753b3a8307964e0da4a7ff2f36d9ec302025-01-19T04:04:24ZengFerdowsi University of MashhadComputer and Knowledge Engineering2538-54532717-41232024-12-0172657410.22067/cke.2024.89572.112745899Anomaly Detection in IoMT Environment Based on Machine Learning: An OverviewPeyman Vafadoost Sabzevar0Hamidreza Rokhsati1Alireza Chamansara2Ahmad Hajipour3Biomedical Engineering Department, Electrical and Computer Faculty, Hakim Sabzevari University, Sabzevar, Iran.Department of Computer, Control and Management Engineering, Sapienza University, Rome, Italy.Department of Biomedical Engineering, Materials and Energy Research Center, Tehran, Iran.Department of Biomedical Engineering, Hakim Sabzevari University, Sabzevar, Iran.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.https://cke.um.ac.ir/article_45899_d051a3bf558ccf20a5b47e541d03d925.pdfanomalydetection anomalyinternet of medical thingsisolation forestunsupervised learning |
| spellingShingle | Peyman Vafadoost Sabzevar Hamidreza Rokhsati Alireza Chamansara Ahmad Hajipour Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview Computer and Knowledge Engineering anomaly detection anomaly internet of medical things isolation forest unsupervised learning |
| title | Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview |
| title_full | Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview |
| title_fullStr | Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview |
| title_full_unstemmed | Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview |
| title_short | Anomaly Detection in IoMT Environment Based on Machine Learning: An Overview |
| title_sort | anomaly detection in iomt environment based on machine learning an overview |
| topic | anomaly detection anomaly internet of medical things isolation forest unsupervised learning |
| url | https://cke.um.ac.ir/article_45899_d051a3bf558ccf20a5b47e541d03d925.pdf |
| work_keys_str_mv | AT peymanvafadoostsabzevar anomalydetectioniniomtenvironmentbasedonmachinelearninganoverview AT hamidrezarokhsati anomalydetectioniniomtenvironmentbasedonmachinelearninganoverview AT alirezachamansara anomalydetectioniniomtenvironmentbasedonmachinelearninganoverview AT ahmadhajipour anomalydetectioniniomtenvironmentbasedonmachinelearninganoverview |