Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach

Authors

Shaikh Afnan Birahim, University of Glasgow
Avijit Paul, Rajshahi University of Engineering and Technology
Fahmida Rahman, International Islamic University Chittagong
Yamina Islam, International Islamic University Chittagong
Tonmoy Roy, Utah State University
Mohammad Asif Hasan, Rajshahi University of Engineering and Technology
Fariha Haque, Rajshahi University of Engineering and Technology
Muhammad E. H. Chowdhury, Qatar University

Document Type

Article

Journal/Book Title/Conference

IEEE Access

Author ORCID Identifier

Shaikh Afnan Birahim https://orcid.org/0000-0003-4358-2939

Tonmoy Roy https://orcid.org/0000-0002-0757-5523

Fariha Haque https://orcid.org/0009-0000-8886-4943

Muhammad E. H. Chowdhury https://orcid.org/0000-0003-0744-8206

Volume

13

Publisher

Institute of Electrical and Electronics Engineers

Publication Date

1-10-2025

Journal Article Version

Version of Record

First Page

13711

Last Page

13730

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 License.

Abstract

Wireless Sensor Networks (WSN) play a pivotal role in various domains, including monitoring, security, and data transmission. However, their susceptibility to intrusions poses a significant challenge. This paper proposes a novel Intrusion Detection System (IDS) leveraging Particle Swarm Optimization (PSO) and an ensemble machine learning approach combining Random Forest (RF), Decision Tree (DT), and K-Nearest Neighbors (KNN) models to enhance the accuracy and reliability of intrusion detection in WSNs. The system addresses key challenges such as the imbalanced nature of datasets and the evolving complexity of network attacks. By incorporating Synthetic Minority Oversampling Technique Tomek (SMOTE-Tomek) techniques to balance the dataset and employing explainable AI methods such as Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), the proposed model achieves significant improvements in detection accuracy, precision, recall, and F1 score while providing clear, interpretable results. Extensive experimentation on WSN-DS dataset demonstrates the system’s efficacy, achieving an accuracy of 99.73%, with precision, recall, and F1 score values of 99.72% each, outperforming existing approaches. This work offers a robust, scalable solution for securing WSNs, contributing to both academic research and practical applications.

Recommended Citation

S. Afnan Birahim et al., "Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach," in IEEE Access, vol. 13, pp. 13711-13730, 2025, doi: 10.1109/ACCESS.2025.3528341.