A hybrid machine learning model for intrusion detection in wireless sensor networks leveraging data balancing and dimensionality reduction

Scientific Reports, Journal Year: 2025, Volume and Issue: 15(1)

Published: Feb. 7, 2025

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) data balancing principal component analysis (PCA) dimensionality reduction, evaluated using the WSN-DS TON-IoT datasets. The employs classifiers such as Decision Tree Classifier, Random Forest Classifier (RFC), gradient boosting techniques like XGBoost (XGBC) to enhance accuracy efficiency. proposed (KMS + PCA RFC) approach achieves remarkable performance, with an 99.94% f1-score on dataset. For dataset, it 99.97% 99.97%, outperforming traditional SMOTE TomekLink Generative Adversarial Network-based techniques. addresses class imbalance high-dimensionality challenges, providing scalable robust intrusion detection. Complexity reveals reduces training prediction times, making suitable real-time applications.

Language: Английский

Scalable and Distributed Cloud Continuum Orchestration for Next-Generation IoT Applications: Latest Advances and Prospects

Future Internet, Journal Year: 2025, Volume and Issue: 17(4), P. 141 - 141

Published: March 25, 2025

With the advent of Internet Things (IoT), centralized cloud computing service delivery paradigm has been gradually transformed into a continuum that includes edge and fog heterogeneous IoT devices with varying power capabilities [...]

Language: Английский