Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach
<p dir="ltr">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) leveragin...
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2025
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| _version_ | 1864513538512388096 |
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| author | Shaikh Afnan Birahim (22303750) |
| author2 | Avijit Paul (22303753) Fahmida Rahman (7517585) Yamina Islam (22303756) Tonmoy Roy (21485527) Mohammad Asif Hasan (22303759) Fariha Haque (21485518) Muhammad E. H. Chowdhury (14150526) |
| author2_role | author author author author author author author |
| author_facet | Shaikh Afnan Birahim (22303750) Avijit Paul (22303753) Fahmida Rahman (7517585) Yamina Islam (22303756) Tonmoy Roy (21485527) Mohammad Asif Hasan (22303759) Fariha Haque (21485518) Muhammad E. H. Chowdhury (14150526) |
| author_role | author |
| dc.creator.none.fl_str_mv | Shaikh Afnan Birahim (22303750) Avijit Paul (22303753) Fahmida Rahman (7517585) Yamina Islam (22303756) Tonmoy Roy (21485527) Mohammad Asif Hasan (22303759) Fariha Haque (21485518) Muhammad E. H. Chowdhury (14150526) |
| dc.date.none.fl_str_mv | 2025-01-23T09:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2025.3528341 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Intrusion_Detection_for_Wireless_Sensor_Network_Using_Particle_Swarm_Optimization_Based_Explainable_Ensemble_Machine_Learning_Approach/30198175 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Intrusion detection system wireless sensor networks particle swarm optimization ensemble machine learning explainable AI streamlit web application Wireless sensor networks Accuracy Intrusion detection Computational modeling Adaptation models Random forests Nearest neighbor methods Monitoring Feature extraction Radio frequency |
| dc.title.none.fl_str_mv | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3528341" target="_blank">https://dx.doi.org/10.1109/access.2025.3528341</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c4db23b26dcc94eee56f8e01340d08d5 |
| identifier_str_mv | 10.1109/access.2025.3528341 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/30198175 |
| publishDate | 2025 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning ApproachShaikh Afnan Birahim (22303750)Avijit Paul (22303753)Fahmida Rahman (7517585)Yamina Islam (22303756)Tonmoy Roy (21485527)Mohammad Asif Hasan (22303759)Fariha Haque (21485518)Muhammad E. H. Chowdhury (14150526)Information and computing sciencesArtificial intelligenceCybersecurity and privacyMachine learningIntrusion detection systemwireless sensor networksparticle swarm optimizationensemble machine learningexplainable AIstreamlit web applicationWireless sensor networksAccuracyIntrusion detectionComputational modelingAdaptation modelsRandom forestsNearest neighbor methodsMonitoringFeature extractionRadio frequency<p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" target="_blank">https://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2025.3528341" target="_blank">https://dx.doi.org/10.1109/access.2025.3528341</a></p>2025-01-23T09:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2025.3528341https://figshare.com/articles/journal_contribution/Intrusion_Detection_for_Wireless_Sensor_Network_Using_Particle_Swarm_Optimization_Based_Explainable_Ensemble_Machine_Learning_Approach/30198175CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/301981752025-01-23T09:00:00Z |
| spellingShingle | Intrusion Detection for Wireless Sensor Network Using Particle Swarm Optimization Based Explainable Ensemble Machine Learning Approach Shaikh Afnan Birahim (22303750) Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Intrusion detection system wireless sensor networks particle swarm optimization ensemble machine learning explainable AI streamlit web application Wireless sensor networks Accuracy Intrusion detection Computational modeling Adaptation models Random forests Nearest neighbor methods Monitoring Feature extraction Radio frequency |
| status_str | publishedVersion |
| 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 | Information and computing sciences Artificial intelligence Cybersecurity and privacy Machine learning Intrusion detection system wireless sensor networks particle swarm optimization ensemble machine learning explainable AI streamlit web application Wireless sensor networks Accuracy Intrusion detection Computational modeling Adaptation models Random forests Nearest neighbor methods Monitoring Feature extraction Radio frequency |