A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT
Abstract Internet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection...
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| Format: | Article |
| Language: | English |
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Wiley
2024-12-01
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| Series: | IET Communications |
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| Online Access: | https://doi.org/10.1049/cmu2.12833 |
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| author | Arash Salehpour Monire Norouzi Mohammad Ali Balafar Karim SamadZamini |
| author_facet | Arash Salehpour Monire Norouzi Mohammad Ali Balafar Karim SamadZamini |
| author_sort | Arash Salehpour |
| collection | DOAJ |
| description | Abstract Internet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud‐edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost‐based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine‐tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW‐NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy. |
| format | Article |
| id | doaj-art-0d312693961d49e2b6fff85082f83eea |
| institution | Kabale University |
| issn | 1751-8628 1751-8636 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Communications |
| spelling | doaj-art-0d312693961d49e2b6fff85082f83eea2024-12-02T05:06:40ZengWileyIET Communications1751-86281751-86362024-12-0118191371139010.1049/cmu2.12833A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMTArash Salehpour0Monire Norouzi1Mohammad Ali Balafar2Karim SamadZamini3Department of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz IranDepartment of Computer Engineering Faculty of Engineering Haliç University Istanbul TürkiyeDepartment of Computer Engineering Faculty of Electrical and Computer Engineering University of Tabriz Tabriz IranDepartment of Computer Engineering University College of Nabi Akram Tabriz IranAbstract Internet of Medical Things have vastly increased the potential for remote patient monitoring, data‐driven care, and networked healthcare delivery. However, the connectedness lays sensitive patient data and fragile medical devices open to security threats that need robust intrusion detection solutions within cloud‐edge services. Current approaches need modification to be able to handle the practical challenges that result from problems with data quality. This paper presents a hybrid intrusion detection framework that enhances the security of IoMT networks. There are three modules in the design. First, an XGBoost‐based noise detection model is used to identify data anomalies. Second, adaptive resampling with ADASYN is done to fine‐tune the class distribution to address class imbalance. Third, ensemble learning performs intrusion detection through a Random Forest classifier. This stacked model coordinates techniques that filter noise and preprocess imbalanced data, identifying threats with high accuracy and reliability. These results are then experimentally validated on the UNSW‐NB15 benchmark to demonstrate effective detection under realistically noisy conditions. The novel contributions of the work are a new hybrid structural paradigm coupled with integrated noise filtering, and ensemble learning. The proposed advanced oversampling with ADASYN gives a performance that surpasses all others with a reported 92.23% accuracy.https://doi.org/10.1049/cmu2.12833internet of thingsresource allocation |
| spellingShingle | Arash Salehpour Monire Norouzi Mohammad Ali Balafar Karim SamadZamini A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT IET Communications internet of things resource allocation |
| title | A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT |
| title_full | A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT |
| title_fullStr | A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT |
| title_full_unstemmed | A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT |
| title_short | A cloud‐based hybrid intrusion detection framework using XGBoost and ADASYN‐Augmented random forest for IoMT |
| title_sort | cloud based hybrid intrusion detection framework using xgboost and adasyn augmented random forest for iomt |
| topic | internet of things resource allocation |
| url | https://doi.org/10.1049/cmu2.12833 |
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