ArticleScientific reports2025
A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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7 citing papers in PubMed.
- Deep hybrid architecture for multi-class detection of network layer attacks in WSN.Scientific reports · 2026Article
- Machine-Learning-Assisted Carbon Dots: From Algorithms to Applications and Beyond.Molecules (Basel, Switzerland) · 2026Review
- Integrating Metabolomics and Machine Learning for Advanced Chemical Detection.Sensors (Basel, Switzerland) · 2026Review
- Intrusion detection with HACDT-Net and TRBM-Net using a hybrid deep learning framework with enhanced sampling techniques.Scientific reports · 2026Article
- Optimizing IoT intrusion detection with cosine similarity based dataset balancing and hybrid deep learning.Scientific reports · 2025Article
- Predicting drug-target interactions using machine learning with improved data balancing and feature engineering.Scientific reports · 2025Article
- Enhancing intrusion detection in wireless sensor networks using a Tabu search based optimized random forest.Scientific reports · 2025Article
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3 authors.
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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.
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