Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach
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...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10836702/ |
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| author | Shaikh Afnan Birahim Avijit Paul Fahmida Rahman Yamina Islam Tonmoy Roy Mohammad Asif Hasan Fariha Haque Muhammad E. H. Chowdhury |
| author_facet | Shaikh Afnan Birahim Avijit Paul Fahmida Rahman Yamina Islam Tonmoy Roy Mohammad Asif Hasan Fariha Haque Muhammad E. H. Chowdhury |
| author_sort | Shaikh Afnan Birahim |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-55fa7c43ec904c24a6a6162b978bb869 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-55fa7c43ec904c24a6a6162b978bb8692025-02-07T00:01:57ZengIEEEIEEE Access2169-35362025-01-0113137111373010.1109/ACCESS.2025.352834110836702Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning ApproachShaikh Afnan Birahim0https://orcid.org/0000-0003-4358-2939Avijit Paul1Fahmida Rahman2Yamina Islam3Tonmoy Roy4https://orcid.org/0000-0002-0757-5523Mohammad Asif Hasan5Fariha Haque6https://orcid.org/0009-0000-8886-4943Muhammad E. H. Chowdhury7https://orcid.org/0000-0003-0744-8206School of Computer Science and Engineering, University of Glasgow, Glasgow, U.K.Department of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, BangladeshDepartment of Computer Science and Engineering, International Islamic University Chittagong, Chittagong, BangladeshDepartment of Data Analytics and Information Systems, Utah State University, Logan, UT, USADepartment of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Electronics and Telecommunication Engineering, Rajshahi University of Engineering and Technology, Rajshahi, BangladeshDepartment of Electrical Engineering, Qatar University, Doha, QatarWireless 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.https://ieeexplore.ieee.org/document/10836702/Intrusion detection systemwireless sensor networksparticle swarm optimizationensemble machine learningexplainable AIstreamlit web application |
| spellingShingle | Shaikh Afnan Birahim Avijit Paul Fahmida Rahman Yamina Islam Tonmoy Roy Mohammad Asif Hasan Fariha Haque Muhammad E. H. Chowdhury Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach IEEE Access Intrusion detection system wireless sensor networks particle swarm optimization ensemble machine learning explainable AI streamlit web application |
| title | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach |
| title_full | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach |
| title_fullStr | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach |
| title_full_unstemmed | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach |
| title_short | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach |
| title_sort | intrusion detection for wireless sensor network using particle swarm optimization based explainable ensemble machine learning approach |
| topic | Intrusion detection system wireless sensor networks particle swarm optimization ensemble machine learning explainable AI streamlit web application |
| url | https://ieeexplore.ieee.org/document/10836702/ |
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