A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction
Abstract Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data balancing and principal compon...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-025-87028-1 |
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| author | Md. Alamin Talukder Majdi Khalid Nasrin Sultana |
| author_facet | Md. Alamin Talukder Majdi Khalid Nasrin Sultana |
| author_sort | Md. Alamin Talukder |
| collection | DOAJ |
| description | Abstract Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data balancing and principal component analysis (PCA) for dimensionality reduction, evaluated using the WSN-DS and TON-IoT datasets. The model employs classifiers such as Decision Tree Classifier, Random Forest Classifier (RFC), and gradient boosting techniques like XGBoost (XGBC) to enhance detection accuracy and efficiency. The proposed hybrid (KMS + PCA + RFC) approach achieves remarkable performance, with an accuracy of 99.94% and an f1-score of 99.94% on the WSN-DS dataset. For the TON-IoT dataset, it achieves 99.97% accuracy and an f1-score of 99.97%, outperforming traditional SMOTE TomekLink and Generative Adversarial Network-based data balancing techniques. This hybrid approach addresses class imbalance and high-dimensionality challenges, providing scalable and robust intrusion detection. Complexity analysis reveals that the proposed model reduces training and prediction times, making it suitable for real-time applications. |
| format | Article |
| id | doaj-art-9b648893af954bcbbc42d7089fefe3b2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | Nature Portfolio |
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| series | Scientific Reports |
| spelling | doaj-art-9b648893af954bcbbc42d7089fefe3b22025-02-09T12:34:12ZengNature PortfolioScientific Reports2045-23222025-02-0115112310.1038/s41598-025-87028-1A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reductionMd. Alamin Talukder0Majdi Khalid1Nasrin Sultana2Department of Computer Science and Engineering, International University of Business Agriculture and TechnologyDepartment of Computer Science and Artificial Intelligence, College of Computing, Umm Al-Qura UniversityDepartment of ICT, Future Technology, RMIT UniversityAbstract Intrusion detection systems are essential for securing wireless sensor networks (WSNs) and Internet of Things (IoT) environments against various threats. This study presents a novel hybrid machine learning (ML) model that integrates KMeans-SMOTE (KMS) for data balancing and principal component analysis (PCA) for dimensionality reduction, evaluated using the WSN-DS and TON-IoT datasets. The model employs classifiers such as Decision Tree Classifier, Random Forest Classifier (RFC), and gradient boosting techniques like XGBoost (XGBC) to enhance detection accuracy and efficiency. The proposed hybrid (KMS + PCA + RFC) approach achieves remarkable performance, with an accuracy of 99.94% and an f1-score of 99.94% on the WSN-DS dataset. For the TON-IoT dataset, it achieves 99.97% accuracy and an f1-score of 99.97%, outperforming traditional SMOTE TomekLink and Generative Adversarial Network-based data balancing techniques. This hybrid approach addresses class imbalance and high-dimensionality challenges, providing scalable and robust intrusion detection. Complexity analysis reveals that the proposed model reduces training and prediction times, making it suitable for real-time applications.https://doi.org/10.1038/s41598-025-87028-1Intrusion detection systemsWireless sensor networksInternet of ThingsHybrid machine learningModelDimensionality reduction |
| spellingShingle | Md. Alamin Talukder Majdi Khalid Nasrin Sultana A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction Scientific Reports Intrusion detection systems Wireless sensor networks Internet of Things Hybrid machine learning Model Dimensionality reduction |
| title | A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction |
| title_full | A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction |
| title_fullStr | A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction |
| title_full_unstemmed | A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction |
| title_short | A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction |
| title_sort | hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction |
| topic | Intrusion detection systems Wireless sensor networks Internet of Things Hybrid machine learning Model Dimensionality reduction |
| url | https://doi.org/10.1038/s41598-025-87028-1 |
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